literature review on fashion trends

International Journal of Interdisciplinary Research

• Open access
• Published: 15 September 2021

Fashion informatics of the Big 4 Fashion Weeks using topic modeling and sentiment analysis

• Yeong-Hyeon Choi 1 ,
• Seungjoo Yoon 2 ,
• Bin Xuan 3 ,
• Sang-Yong Tom Lee 4 &
• Kyu-Hye Lee   ORCID: orcid.org/0000-0002-7468-0681 5  

Fashion and Textiles volume  8 , Article number:  33 ( 2021 ) Cite this article

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This study used several informatics techniques to analyze consumer-driven social media data from four cities (Paris, Milan, New York, and London) during the 2019 Fall/Winter (F/W) Fashion Week. Analyzing keywords using a semantic network analysis method revealed the main characteristics of the collections, celebrities, influencers, fashion items, fashion brands, and designers connected with the four fashion weeks. Using topic modeling and a sentiment analysis, this study confirmed that brands that embodied similar themes in terms of topics and had positive sentimental reactions were also most frequently mentioned by the consumers. A semantic network analysis of the tweets showed that social media, influencers, fashion brands, designers, and words related to sustainability and ethics were mentioned in all four cities. In our topic modeling, the classification of the keywords into three topics based on the brand collection’s themes provided the most accurate model. To identify the sentimental evaluation of brands participating in the 2019 F/W Fashion Week, we analyzed the consumers’ sentiments through positive, neutral, and negative reactions. This quantitative analysis of consumer-generated social media data through this study provides insight into useful information enabling fashion brands to improve their marketing strategies.

Introduction

When launching a new clothing product, fashion houses, brands, and designers must analyze the latest fashion trends by investigating the changes in consumer behavior, market environment, and fashion information. The clothing presented in a fashion house’s collection is a conglomeration of the product, target market, and fashion trends, which also reflects the identity of the designer. Despite developments in computer-based text analysis, social media data from sites such as Twitter, Facebook, Instagram, etc. were generally considered incomplete; however, they are presently valued as new sources of marketing information. Consumers use social media to voluntarily express thoughts regarding their experiences, impression of the products, and observations through posts and likes. As social media encourages users to generate new content, they are valuable platforms for businesses to elicit direct responses from the customers about their products.

In the past, most researchers would ask their focus group participants to tweet their thoughts, which were later analyzed (Kendall, 2014 ). However, social media comments were considered insufficient and unsuitable for a quantitative analysis. Therefore, to compensate its limitations, researchers started including surveys to gather the users’ opinions about products. However, the survey method is also limited because analyzing the information gathered through surveys is extremely time-consuming and the responses that deviate from the questions have to be excluded (An & Park, 2017 ). Therefore, the research methods used in informatics, such as social network analysis, sentiment analysis, and topic analysis are increasingly receiving greater scholarly attention, especially for analyzing social media content (Hong & Oh, 2016 ; Jung & Oh, 2016 ; Lee et al., 2018 ).

The fashion week is a major fashion event featuring various design features of the latest fashion products. This event is quite complex, as it not only involves clothing, but also a variety of participants from the fashion industry such as fashion houses, designers, models, buyers, celebrities, journalists, and customers (Entwistle & Rocamora, 2006 ). Therefore, an analysis of the fashion week requires researchers to consider and analyze all these participants using a variety of methods. Many fashion consumers use smart devices, including mobile phones, to search, share, and even create real-time fashion trends (Jennings, 2019 ). Consumer-driven data enables the fashion industry to explore new ways of forecasting trends and conducting consumer evaluations (Chaudhuri, 2018 ); therefore, this study uses such data to investigate the consumers’ perception and evaluation of the Big 4 Fashion Weeks, held in 2019.

In particular, this paper aims to analyze the city-wise fashion trends in terms of their design features and customers’ perceptions and evaluations. Our informatics methodology employs three approaches of text-based big data analytics: semantic network analysis, topic modeling, and sentiment analysis, to explore the trends and patterns revealed through the participants’ reactions expressed through tweets regarding the four largest fashion weeks in the world (Paris, Milan, New York, and London). To achieve these goals, our study includes three steps: (1) identifying the keywords that appeared in the 2019 F/W Fashion Week through a social network analysis, (2) a city-wise examination of the themes and topics associated with the collections through topic modeling, and (3) analyzing the sentimental evaluation of the brands that participated in the Fashion Weeks through a sentiment analysis.

This study provides insights on prominent influencers, successful brands and designers, popular fashion trends, and the design features preferred by consumers. Using new informatics approaches, this study contributes toward a more in-depth understanding of the participants from the fashion weeks. This event is particularly relevant because it provides valuable communication opportunities to the key players in the fashion industry, and our findings provide several theoretical and practical implications for the field of fashion communication and marketing.

Literature review

Fashion week and latest fashion trends.

Fashion is a style of clothing, footwear, accessories, or makeup that is adopted by a wide audience during a particular time (Hidayati et al., 2014 ). Changes in fashion are important markers for understanding the society, and the “fashion week” is a significant driver of fashion trends in today’s world. A fashion week represents a major event in the fashion industry, which involves fashion designers and brands displaying their collections to the buyers and the media in runway fashion shows (Apparel Search, n.d.). These events influence the trends in the current and the upcoming seasons. The most prominent fashion weeks are held in the four fashion capitals of the world: New York, London, Milan, and Paris. Generally beginning in New York and ending in Paris, the fashion week also known as “the collections,” showcases the upcoming season’s prêt-à-porter clothing (Entwistle & Rocamora, 2006 ). These types of events are geo-localized within a city or a territory and are scheduled on a public calendar. Accordingly, to broaden the scope of our analysis, this study’s focus area includes fashion weeks from all four cities showcasing their best ready-to-wear collections.

The New York Fashion Week is based on a much older series of events called the “Press Week,” founded in 1943. New York was the first city to begin organizing seasonal shows; however, the very concept of a “fashion show,” originated in Paris (Crenshaw, 2019 ), which began holding couture shows since 1945 (Wiig, 2017 ). The Paris Fashion Week was first organized in 1973 under the auspices of the French Fashion Federation (Leaper, 2016 ). The Milan Fashion Week was founded by the Italian Chamber of Commerce in 1958 (Davies, 2013 ), and the London Fashion Week was founded by the British Fashion Council in 1984 (Luu, 2009 ). The Council of Fashion Designers of America created the modern notion of a centralized “New York Fashion Week” in 1993, although cities like London were already using their city’s name in conjunction with the words “fashion week” since the 1980s (Issahaku, 2019 ). The inter-seasonal collections are called Resort/Cruise (typically showcased before Spring/Summer) and Pre-Fall (typically showcased before the F/W season; Cora, 2018 ). Traditionally, designers would usually showcase their autumn and winter collections in February and March, and the Spring/Summer collections in September and October (Cafaro, 2020 ). However, in the recent years, technological advances have changed this schedule as fashion shows have begun to feature garments that are immediately available for sale in both online and physical stores. The present day “see now, buy now” system features clickable videos on their website, where the latest looks are available online immediately, or even during a live show (Pike, 2016 ).

Several scholars have discussed the importance of analyzing the Big 4 Fashion Weeks. For instance, Zhao and Min ( 2019 ) have claimed that analyzing the fashion week can help fashion companies to revolutionize their designs based on the feedback from the analysis. Kim and Lee ( 2019a , 2019b ) explained that the fashion week is a means of communication between fashion brands and the consumers, warranting an in-depth analysis as a valuable communication channel. Additionally, Skov et. al. ( 2009 ) claimed that although the fashion week is essential to the fashion industry, it has also become a cultural icon in its own right. Therefore, it is not just an event to showcase fashion trends, but also an opportunity to identify contemporary cultural and social trends. Simultaneously, the fashion week is also a form of visual merchandising where the fashion shows constitutes a major marketing method used at all the levels of the fashion industry. Analyzing the fashion week helps brands to develop new insights about their customers’ preferences and enables them to effectively prepare for the following seasons.

Furthermore, social media is extremely important for communicating planned events such as fairs, exhibits, and festivals to fans, interest groups, and the general population (Brambilla et al., 2017 ). Social media has greatly enabled consumers to share real-time responses to the fashion week, making their text data (consumer-driven data) more abundant and relevant to the fashion industry. In fact several brands in the fashion industry have used consumers’ social media data to identify upcoming fashion trends (An & Park, 2020 ) and to evaluate their marketing performance (Heo & Lee, 2019 ). Accordingly, fashion communication has evolved from a top-down structure to a more democratic form of communication (Zhao & Min, 2019 ). The fashion week has a variety of implications, wherein a the trickle-down phenomenon popularizes new fashion trends that designers and celebrities present at the fashion week, and the consumers’ opinions of the fashion week in turn provides valuable marketing feedback to the brands.

Traditionally, most analyses concerning fashion trends have used qualitative case studies that were conducted based on the interpretation of the experts. Moreover, such analyses primarily focused on fashion collections, but did not consider the perspectives of consumer preferences or the performance of the fashion brands. The reliability and validity of this approach depended on the personal abilities of the fashion expert group or the researchers. Therefore, using a consumer-driven data approach makes it possible to produce different results with the same data while incorporating individual perspectives. Conversely, in the field of informatics, computer scientists have devoted considerable effort on studying the different methodologies for analyzing and predicting fashion trends (Baskerville & Myers, 2009 ; Hidayati et al., 2014 ; Lee et al., 2017 ; Singh et al., 2019 ). However, as most of them lacked the background knowledge in fashion, their focus was restricted to technological methods, limiting their ability to generate new insights from their results. Therefore, to address these limitations, fashion researchers have started using big data analytics to assess fashion brands based on their analysis and the predictions of the consumers’ reactions to various fashion collections (e.g., Silva et al., 2019 ). While these studies were primarily centered on text mining and semantic network analysis, the present study seeks to derive diverse interpretations using more advanced techniques such as topic modeling and sentiment analysis.

Fashion informatics and applications

Fashion trend analysis has attracted considerable attention due to the rapid of growth of online data, leading to considerable improvements in analysis techniques (An & Park, 2020 ). Moreover, the topics that appear in social media reflect the interests of the users and form the basis of public opinion (Lee et al., 2014 ). Therefore, several fashion studies have tapped into the advantages of the increasing accessibility of text mining to analyze consumer responses and fashion trends (e.g., Lang et al., 2020 ). For instance, Choi and Lee ( 2020 ) used the text mining method to determine Korean consumers’ awareness of the new retro fashion (Newtro fashion), and its semantic components. They found that the words “retro,” “new,” “past,” the “1990s,” “reinterpretation,” “big logo,” and “tint glasses” frequently appeared in consumers’ social media posts, indicating major consumer awareness. Lang et. al. ( 2020 ) used text mining to investigate the online fashion renting experiences of consumers and identified that the words “easy,” “recommend,” “process,” “site,” “use,” “special,” “highly,” and “navigate” frequently appeared in positive reviews. In another study, Choi and Lee ( 2021 ) focused on vegan fashion trends while analyzing the changes in the consumers’ awareness of original and artificial fur and leather, and confirmed that the keywords related to animal protection appeared with high frequency in their posts.

Network analysis is a useful method for obtaining new insights when analyzing the Big 4 Fashion Weeks. The relationships between the keywords (edges) can provide important information and much more objective insight on fashion trends than the previous research conducted by expert groups that used qualitative methods. Studies that have applied network analysis mechanisms to analyze fashion collections and fashion week used the following approaches (An & Park, 2020 ; Zhao & Min, 2019 ). Zhao and Min’s ( 2019 ) method for analyzing fashion collections using social media involved collecting the Twitter API before, during, and after a Haute Couture show, which was later subjected to a social network analysis. In fact, our study’s analysis of the fashion week referred to the range and the data collection process used by Zhao and Min’s ( 2019 ), but used a different method and object of analysis from their study. Additionally, Zhao and Min’s ( 2019 ) search keywords were limited to three phrases: “Paris Fashion Week,” “Haute Couture,” and “Chanel Haute Couture,” wherein their research solely focused on Paris’s Haute Couture show to provide an in-depth analysis. In contrast, An and Park’s ( 2020 ) study used text mining and a semantic network analysis to determine the prominent design features from fashion trends through a 10-year data from blog posts that included phrases like “jacket” and “fashion collection.” For a trend analysis, they used a time-series cluster analysis to categorize the fashion trends from the blog posts into four clusters: increasing, decreasing, evergreen, or seasonal. Based on the developments in the existing literature, our study asks the following research questions regarding the Big 4 Fashion Weeks.

Research Question 1. What are the prominent keywords derived from a social network analysis of the 2019 F/W Fashion Weeks?

Our study applies the latent Dirichlet allocation (LDA) algorithm to analyze the fashion collections in the F/W Fashion Weeks. This method focuses on recurring patterns of word occurrence from documents to infer the emergent topics. To date, fashion designers, experts, and editors almost unilaterally determine the themes used by fashion collections. However, with topic modeling, even non-fashion experts can identify alternative and informal themes. Based on these advantages, An and Park ( 2017 ) used the LDA algorithm to analyze the users’ references to men’s striped shirts. Similarly, Jang and Kim ( 2017 ) used the text mining method and the LDA algorithm to analyze the keywords and the research abstracts appearing in the Journal of Fashion Design from 2001 to 2015 to discover the research topics that gained and lost popularity during these years. Based on the aforementioned studies, we concluded that topic modeling techniques are an effective means of extracting a potential classification combining various topics. While some fashion studies have classified and analyzed the fashion trends appearing on social media (e.g., An & Park, 2020 ), our study incorporates a qualitative analysis to the topic modeling technique used in prior studies and uses its results to categorize the themes followed by the fashion collections. Based on this approach our study proposes a new methodology for the analysis of fashion collections.

Research Question 2. What are the city-wise themes emerging from the topic modeling of the fashion collections from the 2019 F/W Fashion Weeks?

The increase in the amount of the text produced in online environments, especially in social media, and the developments in natural language processing technology have led to considerable interdisciplinary research on sentiment analysis (Lee et al., 2018 ). Sentiment analysis is mainly used to analyze public perceptions through online textual data, such as movie and product reviews. Lee et. al. ( 2017 ) conducted a sentiment analysis of the Amazon fashion product reviews and used a Support Vector Machine (SVM) classifier to build a positive–negative analysis model for analyzing user opinion. Expanding on the original binary method of classifying emotions (positive vs. negative), they used seven categories of emotions to analyze public opinion related to artificial intelligence: anger, aversion, fear, happiness, neutrality, sadness, and surprise, and found that the sentiment of the text determined by these seven categories was different from that determined through the binary method. Heo and Lee ( 2019 ) utilized sentiment analysis to analyze trends in the fashion brand Gucci and found discovered a rising brand appreciation since 2015, when Alexandre Michele was hired as creative director. Therefore, we derive the following research question:

Research Question 3. What is the consumer sentiment toward the brands that participated in the 2019 Fall/Winter Fashion Weeks, analyzed through a sentimental evaluation?

Data collection and analysis

We used the open Twitterscraper application programming interface (API) in a Python program to extract Twitter’s user data during the Big 4 Fashion Weeks. The data was crawled using Python 3.7, which was then preprocessed and subjected to a morphological analysis. Table 1 shows the field structure. The search keywords used were “#pfw,” “#mfw,” “#nyfw,” and “#lfw,” which refer to the respective fashion weeks from each city: Paris, Milan, New York, and London. The date ranges used for the data collection was limited to the period in which the 2019 F/W Fashion Weeks were held: New York (February 8–16, 2019), London (February 15–19, 2019), Milan (February 19–25, 2019), and Paris (February 25–March 5, 2019). Our data set had a total of 33,525 records.

For data preprocessing, we used the natural language toolkit (NLTK) provided by Python 3.7. When tokenizing the data, NLTK module’s Tweet Tokenizer was utilized to improve accuracy and to prevent the tokens from losing their meaning when all punctuations and special characters were removed. Stop-words, such as surveys and suffixes that appeared frequently but were not relevant to the analysis were eliminated using the NLTK’s Stop-words module. During the semantic network analysis we used the NodeXL 1.0.1 program to conduct a centrality analysis, topic clustering, and visualization.

Text mining

Text mining is a method of extracting information from unstructured text data, such as posts and user comments. It involves the use of natural language processing, text analysis, and computational linguistics to identify and extract information from the source materials (Kim & Kim, 2014 ). Text mining analyzes a word’s frequency and its probability of occurrence through natural language processing and morphological analysis to identify the links between texts (Callon et al., 1983 ; Srivastava & Sahami, 2009 ). Text mining is a form of machine learning, and involves several analysis techniques, including part-of-speech, degree centrality, and the frequency of occurrence analyzes (Feldman & Sanger, 2007 ). Text mining techniques are both used by themselves (Choi & Lee, 2021 ) and to provide precedent data for social network analysis or sentiment analysis (e.g., Choi & Lee, 2020 ; Lang et al., 2020 ). Likewise, this study applied text mining to provide input for social network analyzes, topic modeling, and sentiment analyses.

Semantic network analysis

Semantic network analysis is a computer-based social network analysis method that uses messages as its object of analysis rather than people (Mitchell, 1969 ). It is a method of deriving the characteristics of a network by detecting the strength and regularity of text-based inputs on frequency and concurrency (Park & Leydesdorff, 2004 ). Within a network, the positions occupied by nodes can be expressed through their centralities (Freeman, 1978 ; Hanneman & Riddle, 2005 ; Scott, 2012 ). The connection centrality, the most employed indicator of the power of a node, refers to the frequency in which one word is connected to another word (Kim & Kim, 2016 ). Nodes with a high value of median centrality act as bridges connecting groups of nodes within the network. In a semantic network, a word that has a high proximity center tends to be more frequently connected with other words (Bavelas, 1950 ; Freeman, 1978 ). The eigenvector centrality is based on the idea that one node connected to another node is important and is determined in proportion to the sum of the centrality values of the nodes directly connected to a particular node. Even if a node has a low centrality, it could still have a high eigenvector centrality, provided that the other connected nodes also have high centralities (Kwahk, 2014 ). In addition, it can set a theme by grouping keywords with similar characteristics through clustering algorithms on a network analysis program (Clauset et al., 2004 ).

The centrality measure can be summarized by standardizing the equation as follows: (1) for each calculation, \(C_{x} \left( {N_{i} } \right)\) is the centrality of actor i ; (2) g is the number of actors ( i ) in the network (Wasserman & Faust, 1994 ); (3) the standardized actor—betweenness centrality, is g divided by the maximum value (( g  − 1)( g  − 2)/2) of the betweenness centrality (Freeman, 1978 ; Wasserman & Faust, 1994 ); (4) in the eigenvector centrality, the actor i is the i th element of eigenvector unit e , and e presents the largest eigen value of the adjacent matrix, with x as an element. X is an adjacent matrix with \(X_{ij}\) as an element, and \(\lambda\) as an array of eigen values (e.g., Bonacich, 2007 ; Kwahk, 2014 ).

Degree centrality of actor i :

Betweenness centrality of actor i :

Closeness centrality of actor i :

Eigenvector centrality of actor i :

• Topic modeling

Topic modeling is based on a statistical inference method that determines the probability of a word’s association with a certain topic, and the joint probability of the topic existing in a particular document. The LDA is one of the several statistical algorithms that can be used for topic modeling. It predicts the related words of a particular topic, based on the premise that documents with similar word distributions will contain similar topics (Blei et al., 2003 ). Blei et. al. ( 2003 ) introduced the LDA as the first approach that allows for modeling of the topic semantics entirely within the Bayesian statistical paradigm. According to their research, the aim of the LDA algorithm is to model a comprehensive representation of the corpus by inferring the latent content variables, called topics. Topics are heuristically located on an intermediate level between the corpus and the document that can be imagined as content-related categories, or clusters. A major advantage of topic modeling is that it requires no prior knowledge input to infer the topics from a given collection. Since topics are inferred and not explicit, no information about them is directly observable in the data.

For topic modeling, we used the Gensim module provided by Python 3.7 and visualized the resulting data using the pyLDAvis module (Fig.  1 ), where: (1) \(K\) represents the number of topics; (2) \(\alpha\) is the value of the Dirichlet prior weight of topic k by document, and is a parameter that determines the value of \(\theta\) ; (3) η is the Dirichlet prior weight of word w by topics, and is a parameter that determines the value \(\beta\) ; (4) \(\theta_{d}\) is the ratio of the topics per document; (5) \(\beta_{k}\) is the probability of generating the word w by topic; (6) \(Z_{d,n}\) is the topic of the n th word of the document d ; and (7) \(W_{d,n}\) is the n th word of document d (observed variable in the document; Kim et al., 2016 ).

(Reprinted from Kim et al. ( 2016 ))

• Sentiment analysis

Sentiment analysis is a text mining technique that extracts the key opinions, emotions, attitudes, and dispositions from a large amount of text data to estimate and classify the author’s emotions (Feldman, 2013 ; Lee et al., 2016 ). In general, the data are classified in a binary form as positive or negative and are further sub-categorized into multi-category sensibilities such as sadness, anger, or happiness. An emotional analysis is carried out through subjectivity detection and a polarity detection (Yeon et al., 2011 ). The text collected in the subjectivity detection stage is then classified into elements to be used for sentiment analysis after eliminating the unrelated terms. In the polarity detection phase, a sentiment dictionary is used to determine whether the given data is positive or negative. Next, the polarity detection is conducted using two methods: machine learning, and emotional dictionaries. Machine learning classifies the contents of a given document as positive or negative through patterns contained in the document; from these manually coded documents, the training data are then extracted, which are used to create machine-based models for determining the emotional valence of the document. Emotional classification analyzes the subjectivity of documents according to an emotional dictionary, which is created by recording the polarity of the predicates that are polar (adjectives) (Oh & Chae, 2015 ).

Using the above methods, we classified each tweet into positive, neutral, and negative emotions using WordNet, which is an English emotional dictionary provided by the Princeton University for sentiment analysis. From the crawled tweets, we extracted the tweets that mentioned fashion brand and used a positive and negative frequency analysis to determine the customers’ responses to each of the brands. Equation  5 presents the formula for the sentimental evaluation of a brand used in this study.

Formula for the sentimental evaluation of a brand:

Semantic network analysis by city

Based on the frequency of appearance, the top 100 keywords, including “designers,” “brands,” “influencers,” “fashion items,” “designs,” “materials,” and “themes,” were extracted, classified, and visualized through the Clauset–Newman–Moore (CNM) algorithm. The macroscopic properties of each network are indicated in Table 2 .

2019 F/W Paris fashion week

A network analysis of Twitter for the 2019 F/W Paris Fashion Week reveals that “Chanel (814),” “Tommy Hilfiger (681),” “Saint Laurent (375),” “Zendaya Coleman (360),” “Dior (328),” “Gigi Hadid (253),” “Balmain (224),” “street fashion (217),” “Miu Miu (207),” and “Off-White (203)” were the most frequently mentioned keywords (Table 3 ). Using the CNM algorithm, the data was classified into the following groups: item groups, social networking sites (SNS) and influencers, brand and designer groups, design formats, and brand groups (Fig.  2 ). The item group included keywords such as “double denim,” “sweat shirt,” “lace bra,” “teddy bear coat,” “fur coat,” “lace skirt,” “leather jacket,” “micro bag,” “boyfriend blazer,” “trench coat,” “bucket hat,” and “boiler suit,” which were connected to the brand and the designer that launched the item. The terms related to SNS and influencers such as “Facebook,” “Instagram,” “Twitter,” and “celebrity” were categorized into the same group. Additionally, among the influencers, models “Gigi Hadid,” “Bella Hadid,” and the celebrities affiliated with YG and SM town were mentioned with the term “K-Pop.”

Classified groups of keyword clusters using CNM Algorithm: 2019 F/W Paris fashion week

Words pertaining to fashion bloggers, such as “fave style,” “fashion blogger,” and “mix and match,” also emerged as keywords even though these terms are not directly related to the fashion week. These keywords emerged because of the fashion bloggers’ repeated reference to the fashion trends and styles presented in the fashion week, which reflects the people’s attention toward the apparel worn in the fashion week. In particular, for terms such as “photograph” or “fashion blogger,” the connection and the eigenvector centers showed higher values than “brands,” “designers,” “celebrities,” and certain fashion items. An eigenvector centrality represents the degree of influential keywords possessed by a node. Therefore, we infer that external factors such as fashion bloggers and their social media activities are influential in the fashion week.

Based on connection-centricity, “Off-White,” “Chanel,” and “Chloe” are the most influential brands. Among fashion items, “micro bag,” “leather jacket,” and “black dress” received the most attention. Important themes and styles associated with the collections presented by the brands are “art deco,” “glam rock,” “sustainable,” and “feminine.” Furthermore, “Parisian” and “Haute Couture” emerged as unique keywords related to the Paris Fashion Week. Unlike the results from Milan, London, and New York, the top keywords mentioned in Paris Fashion Week in Paris were mostly related to fashion brands rather than influencers. Consistent with the claim presented by Joo ( 2016 ), the trends from the Paris Fashion Week indicates that Paris is a traditional fashion city that possesses artistic, multicultural, technical, and industrial distinctions based on the French culture.

2019 F/W Milan fashion week

A network analysis of the 2019 F/W Milan Fashion Week showed that “Gucci (277),” “Versace (275),” “Moschino (225),” “Prada (171),” “street fashion (135),” “Giorgio Armani (121),” “Gigi Hadid (116),” “Karl Lagerfeld (115),” “OOTD (outfit of the day; 108),” and “Max Mara (90)” were the most frequently-appearing keywords (Table 3 ). A classification using the CNM algorithm revealed six groups and three major clusters (Fig.  3 ). Frequently cited words showed similar degree, closeness, betweenness, and eigenvector centralities. The clusters with the most nodes contained references to SNS/fashion bloggers, fashion design sensibilities and elements, and major brands. Interestingly, keywords such as “real-time,” “Facebook,” and “Instagram” all showed connectivity, proving that SNS enabled a rapid proliferation of video and postings about the fashion week.

Classified groups of keyword clusters using CNM Algorithm: 2019 F/W Milan fashion week

Compared to the tweets from other three cities, tweets from the Milan Fashion Week featured more references to the materials and patterns used in the clothes produced, such as “quilt,” “mesh,” “metal,” “sequin,” “velvet,” “leather,” “floral,” and “stripes.” Furthermore, words related to diverse fashion items frequently recurred. According to Joo ( 2016 ), the Milan Fashion Week tends to feature collections that combine both New York's practicality and Paris’ creativity. Therefore, the frequently-mentioned keywords are in line with the practical and the creative aspects of the New York and the Paris collections, respectively. In addition, faux (artificial) fur showed connectivity with the animal protection organization People for the Ethical Treatment of Animals, which was also associated with “animal print.” The results indicate that ethical fashion is becoming a hot topic among fashion brands and designers in Milan.

Three groups contained references to fashion brands and fashion items. Fashion items that were mentioned include “gown dress,” “silk scarf,” “jumpsuit,” “mini dress,” “wide pants,” and “leather jacket,” and related trends such as “grunge,” “retro,” “vintage,” and “glam” also appeared. The emergence of distribution channels such as “online stores” and “pop stores” indicated public interest in popular items presented in the collection. Words not directly related to the collections, such as “Bohemian Rhapsody,” “Queen,” “Spider Man,” “Disney,” “Black Panther,” and “Netflix” appeared in relation to the Oscars Awards due to the celebrities and models who attended the fashion week.

Based on connection-centricity, Versace, Gucci, and Prada are the most influential brands in the Milan Fashion Week, and “mini dress,” “leather jacket,” and “fur coat” were the fashion items that attracted the most attention. These fashion items were closely related to the theme and the sentiment of the respective brands that presented the items. The term “Duomo cathedral” appeared both as the place where the collection was displayed, and as a keyword reflecting the characteristics of the city.

2019 F/W New York fashion week

A network analysis of the 2019 F/W New York Fashion Week reveals that “OOTD” (878), “street fashion” (838), “Michael Kors” (701), “sustainable” (532), “stripe dress” (514), “fashion blogger” (508), “Ralph Lauren” (433), “Marc Jacobs” (356), “Instagram” (346), and “faux fur” (337) were the most frequently cited words (Table 3 ). These keywords were then classified into two groups based on the number of nodes using the CNM algorithm (Fig.  4 ). SNS and celebrities appeared in the same group, whereas other keywords related to design features, items, and brands were spread across different groups.

Classified groups of keyword clusters using CNM Algorithm: 2019 F/W New York fashion week

Olivia Palermo, Kendal Jenner, Gigi Hadid, Barry Manilow, Paris Hilton, and Yoona, and were some of the prominent celebrities/models that appeared in the same group, who were also related to words such as “red carpet” and the “Grammy award.” While fashion bloggers were considerably influential at the Milan Fashion Week, celebrities and models who appeared in the New York Fashion Week were actively mentioned on Twitter with words related to the Grammy Awards. Social media-related keywords appeared in the same group as the celebrities, and “Soho” appeared as a keyword that reflected regional characteristics.

The New York Fashion Week is the largest fashion week. It features popular trends and primarily focuses on practicality rather than innovation, differentiating it from the Paris Fashion Week, which focuses on art and design; Milan, which presents work based on the textile industry; and London, which features innovative designs (Joo, 2016 ). Nevertheless, despite being known for its traditional and commercial designs, the New York Fashion Week featured the highest number of ethical fashion-related keywords among the four cities: “sustainable,” “faux fur,” “climate change,” and “recycled fabric.” This phenomenon suggests that fashion trends in New York have shifted their focus from practical to ethical fashion.

Based on connection-centeredness, fashion brands such as “Michael Kors,” “Tom Ford,” and “Ralph Lauren” are the most influential brands, and “stripe dresses,” “faux coats,” and “leather jackets” were the most popular fashion items. The New York Fashion Week was also associated with style themes such as “chic,” “glam,” and “experimental,” which showed the overall sentiment of the New York collection. Furthermore, among the four fashion weeks, the clothing collections in New York had the most diverse range of colors despite being held in the F/W season, words associated with light and saturated colors, such as “bright color,” “bright yellow,” “fuzzy pink,” “mint green,” and “neon color” frequently appeared. “Simple silhouette” and “wrapped silhouette” were the most commonly cited silhouettes. White shirts and check patterns were the most common in terms of their betweenness centrality, making them the most used designs feature in the New York F/W collection.

2019 F/W London fashion week

Table 3 shows the keywords from a network analysis of Twitter for the 2019 F/W London Fashion Week in the order of their frequencies. The data were classified into three groups using the CNM algorithm. The SNS/influencer group and the design element group were differentiated in distinct groups whereas keywords pertaining to brands, ethical fashion, and celebrities were distributed among three groups (Fig.  5 ).

Classified groups of keyword clusters using CNM Algorithm: 2019 F/W London fashion week

“Instagram,” “YouTube,” “fashion-gram,” “influencer,” “fashion blogger,” and “fashionista” co-occurred in the same group, and terms related to outerwear, such as “leather jacket,” “motorcycle jacket,” “warm jacket,” “winter jacket,” and “blazer” recurred in the item/design group. Design elements such as “motorcycle jacket,” “warm jacket,” “winter jacket,” and “blazer” also co-occurred.

Well-known figures who are not associated with the fashion industry also appeared: soccer player “David Beckham” was mentioned on Twitter as the husband of designer Victoria Beckham, and the British royal household members such as “Queen Elizabeth” and “William Spencer” also appeared. Political keywords such as “Brexit” were also mentioned, as some designers criticized Brexit through their collections. For instance, Vivienne Westwood used her designs to disparage global climate change, Brexit, and fast fashion.

Most keywords that appeared in London Fashion Week are related to the British fashion brands and designers. The degree centrality revealed “Victoria Beckham,” “Burberry,” “Ports 1961,” and “Vivienne Westwood” as the most influential keywords. Terms such as “chic,” “glam,” and “classic” were frequently referenced, and the term “old school” also appeared. Frequently used words also showed high results in degree, betweenness, closeness, and eigenvector centralities.

On Twitter, the London Fashion Week was associated with two keywords with opposing concepts. Keywords related to ethical fashion such as “sustainable,” “environment,” “ecolabel,” “recycled,” “secondhand,” and “positive fashion” also coexisted with animal-based materials such as “leopard,” “snake skin,” “fur,” and clothes with “animal prints.” In particular, the words “positive fashion” and “sustainable” had high betweenness centralities relative to their frequencies, indicating that brands, designers, and items are often referenced in relation to one another.

Topic modeling in 2019 F/W fashion week

To construct a model with sufficient number of topics, we increased the number of topics from two to 50 in increments of 10, and thereafter created a model that increased in increments of one in the interval two to 20. Finally, three topics were selected for each city. Based on articles from Marie Claire ( https://www.marieclaire.com ) and Vogue’s websites ( https://www.vogue.com/fashion-shows ) that have free access to information about fashion shows, we conducted a sentiment analysis to investigate the degree to which the results matched the topics. The results showed similar design inspirations, collection themes, and brands for the same topic. The topics related to people such as the Beckham couple or the Hadid sisters primarily focused on the relationship between people.

F/W Paris fashion week

For the 2019 Paris F/W collection, the following brands had the highest probability of distribution in Topic 1: “Balmain,” “Valentino,” “Off-White.” “Stella McCartney,” “Alexander McQueen.” In Topic 2, “Tommy Now,” “Miu Miu,” “Dior,” “Givenchy,” and “Hermes” appeared, and “Chanel,” “Saint Laurent,” “Chloe,” “Celine,” and “Loewe” had the highest probability of distribution in Topic 3 (Fig.  6 ).

Topic modeling: 2019 F/W Paris fashion week

Topic 1 brands had well-tailored jackets, angled shoulder lines, and funky ambiences as their prominent features, and were characterized as “tailored street style.” Balmain featured a well-tailored uniform jacket, crisp ruffled dress, masculine shoes, and leather pants similar to those worn by pop stars in the 1980s. Valentino presented a funky-style floral print, neon color silk dress, and clothes exhibiting a street mood, while Off-White featured optical patterns, metallic trench coats, jersey dresses with asymmetrical hemlines, and primary color leather coats that centered around street wear and checkerboard prints.

Brand collections under Topic 2 mainly featured clothes athleisure style as exemplified by the brand Tommy Now, which depicted a sporty mood based on supermodels from the 1980s and 1990s and disco queens from the 1970s. Miu Miu exhibited a millennial sentiment expressed through long capes, mini dresses with ruffle decorations, chunky boots, and big bags. Dior combined a tailored jacket having a masculine silhouette with a big skirt, a new look silhouette dress, a sporty jumper, kitten heels, a bucket hat, and cropped suit pants. Brands under topic four exhibited clothes with classic and feminine moods. For instance, Chanel, the most exemplary brand in Topic 3, featured a tweed houndstooth check suit, a candy-colored sweater with a classic Chanel logo, and a feather-detail cocktail dress. The large shoulder line, straight silhouette, and polka-dot tights made the tuxedoes architectural and feminine.

F/W Milan fashion week

For the 2019 Milan F/W collection, “Giorgio Armani,” “Roberto Carvalli,” “Gucci,” “Dolce Gabbana,” and “Alberta Ferretti,” had the highest probability of distribution for Topic 1, whereas Topic 2 comprised of brands such as “Versace,” “Moschino,” “Bottega Veneta,” and models “Gigi Hadid,” “Bella Hadid,” and “Candice Swanepoel” (Fig.  7 ).

Topic modeling: 2019 F/W Milan fashion week

Topic 1 was named “tailored” because the most representative brand in Topic 1, Giorgio Armani, featured clothing with dark and calm colors, complex tailoring, and luxurious materials such as silk, velvet, and leather. Other major brands in Topic 1 also presented clothing that aligned with this theme. For instance, Roberto Carvalli also had elegantly tailored coats, classic neckline dresses, boots that delicately covered people’s legs, and geometric patterns and design. Gucci also displayed colorful genderless designs based on complex tailoring.

Topic 2 was labelled “Retro” the brands featured under this topic expressed a vintage sentiment popular during the 1970s, 1980s, and 1990s. Moschino staged kitsch gold jewelry matched with voluminous retro hair prevalent in the 1970s, along with various monetary patterns, cartoon characters, and humorous baby bear prints. Versace exhibited a grunge look from the 1990s consisting of old-fashioned cut-off detail, messy hair, retro-style bustiers; gorgeous colors like gold, yellow, blue, pink and orange, and utilized silky materials, delicate laces, classic patterns, glam, luxury moods, slip dresses and jackets.

Brands under Topic 3 developed genderless designs featuring masculine and linear silhouettes, and was therefore labelled “Genderless.” Gucci appeared in all the topics due to its elaborate tailoring (Topic 1) retro mood (Topic 2), and genderless silhouettes (Topic 3). Prada harmoniously combined romantic details such as flowers, lace, and ribbons with a military mood with masculine pants, and a goth mood. Missoni expressed a genderless design through classic and geometric patterns and large neck decorations. Max Mara utilized beige, camel, and navy-focused color palettes, a maximal coat, multiple-layered jackets, and a professional-looking suit.

F/W New York fashion week

Based on the data collected from the New York Fashion Week, brands such as “Marc Jacobs,” “Prabal Gurung,” “Tom Ford,” and “Tory Burch” were placed under Topic 1. Topic 2 was mostly comprised of words regarding social media influencers, such as “fashion blogger,” “photography,” “OOTD,” “front-row,” “fashionista,” “social media,” and “celebrity,” rather than brand names, excluding “Ralph Lauren.” Topic 3 featured brands such as “Michael Kors,” “Coach 1941,” “Jeremy Scott,” and “Longchamp” (Fig.  8 ).

Topic modeling: 2019 F/W New York fashion week

Topic 1 was labelled “Classic and romantic” because it was characterized by various design details, fashion items, and classic and romantic moods. Marc Jacobs’ collection had rich silhouettes, a classic cape and coat, a couture dress decorated with artificial feathers and flowers, and a feather dress. Prabal Gurung presented a look that displayed an oriental style, contrasting color matches reminiscent of Yves Saint Laurent and Christian Lacroix, ethnic jacquard fabrics, bold fringes, and oriental embroidery. Tom Ford expressed sensuality through a pale blue shirt, purple satin pants, silk jersey, and a finale dress paired with a bold chain. Tory Burch presented classic, tailored coats and trousers, masculine silhouettes, floral patterns, and ruffles, along with pleated romantic blouses and skirts.

Ralph Lauren, which appeared under Topic 2, presented an elegant and dramatic look that displayed a military mood, a sailor look, and embodied American luxury through a color palette consisting of black, gold and white. However, Topic 2 mostly comprised of keywords such as “fashion blogger,” “photography,” “OOTD,” “front-row,” “fashionista,” “social media,” and “celebrity.” Therefore, Topic 2 was labelled “influencer.”

The common characteristics of the brands shown in Topic 3 were a street mood with a 1970s and 1980s vibe, and leather jackets. Topic 3 was therefore named “Vintage and Retro.” Michael Kors portrayed designs popular during the 1970s through a floral dress that featured a shearing fur stole, fringes, beads, a slip dress with feathers, a check pants suit, Boeing glasses, and a newsboy cap. In contrast, Coach 1941, displayed a bohemian mood with a psychedelic quilted pattern, a floral and a marbling pattern reminiscent of geological layers, classic tartan checks, and Bermuda pants. Jeremy Scott utilized black and white colors, exaggerated accessories such as a gigantic ribbon, an A-line tulle, a rider jacket, and a hoodie to convey a 1970s and 1980s sentiment. Furthermore, Longchamp expressed a similar sentiment through its rock-style leather clothing, animal prints, ethnic patterns, signature logo patterns, and a miniature-sized white Le Pliage bag.

F/W London fashion week

The data from the 2019 London Fashion Week indicated that brands such as “Victoria Beckham,” “Simone Rocha,” “J.W. Anderson,” “Peter Pilotto,” “Roksanda,” and “Christopher Kane” were most associated with Topic 1. Topic 2 included keywords such as “Burberry,” “Vivienne Westwood,” “Alexa Chung,” “Ports 1961,” “Ashish,” and “Mary Katrantzou,” and Topic 3 featured the words “Richard Malone,” “sustainable,” and “recycled.” The brands that appeared in Topic 1 expressed a modern and feminine businesswoman, or displayed a romantic look frequently associated with young girls. Therefore, Topic 1 was named “Businesswoman and Romantic.” Victoria Beckham presented a businesswoman look through refined beauty, checkered pencil skirts, flare pants, and silk shirts. Furthermore, her look featured smooth and feminine colors such as lilac, turquoise blue and red. A cape-shaped manic silhouette coat with a pointed long collar and a wide banding at the waist portrayed a modern and feminine sensibility. Simone Rocha harmoniously combined an abstract and a bizarre sentiment conveyed through swirly patterns and rich balloon silhouette dresses with cobwebs with an opposing sense of romanticism. J.W. Anderson presented a voluminous dress in colorful chiffon, a flat patterned jacket, and oversized choker chains and belts (Fig.  9 ).

Topic modeling: 2019 F/W London fashion week

In Topic 2, brands exhibited a combination of various themes and asymmetrical silhouettes. Therefore, Topic 2 was named “Heterogeneous and Unbalanced.” Burberry, a brand that had the highest probability of being included in Topic 2, presented two distinct concepts. In the boys and girls section, it presented a street look with sportswear, lingerie dresses, and track pants, whereas in the Gentleman and Lady section, it included formal and classic items such as trench coats and tailored jackets to express the coexistence of classical and punk styles. Vivienne Westwood unveiled its signature wool coats with signature check patterns, painted-print leggings and jackets, and satin dresses, along with messages criticizing Brexit, global climate change, and fast fashion. Alexa Chung featured silhouettes that were popular during the 1940s through ruffles and glossy materials such as velvet and leather with floral patterns. In Topic 3, Richard Malone was the only fashion house that was included, and the topic was named “Sustainability” because it appeared with words such as “sustainable” and “recycled.” Accordingly, Richard Malone’s collection used organic cotton and recyclable materials that were sustainable and experimental (Table 4 ).

Sentiment analysis in 2019 F/W fashion week by brands

To perform a sentiment analysis of the Big 4 Fashion Weeks, we first extracted the nouns from the tweets crawled from Twitter, through stemming and listed them by word frequency. Thereafter, we categorized the three of the most frequently-mentioned brands for each city (shown in Table 1 ) as follows: Paris Fashion Week—Chanel (814), Dior (328), and Balmain (224); Milan Fashion Week—Gucci (277), Versace (275), and Prada (171); New York Fashion Week—Michael Kors (701), Ralph Lauren (433) and Marc Jacobs (356); and London Fashion Week—Burberry (298), Victoria Beckham (295), and Vivienne Westwood (120) (Table 5 ).

The positive tweets pertaining to brand names extracted referenced brands such as Chanel (293), Dior (154), and Balmain (91) related to the Paris Fashion Week; Gucci (111), Versace (99) and Prada (73) appeared in tweets related to the Milan Fashion Week; Michael Kors (455), Ralph Lauren (190), and Marc Jacobs (138) appeared in tweets related to the New York Fashion Week; and Burberry (116), Victoria Beckham (148) and Vivienne Westwood (37) related to the London Fashion Week.

The results of the analysis indicate how these brands induced positive reactions from Twitter users (Table 6 ). The positiveness of a brand’s tweet’s was calculated through the percentage of positive tweets among the total number of tweets, calculated using Eq.  5 . The resulting number is a quantitative representation of the sentimental evaluation of the brand. The top-ranked brands mentioned also had different positive rates depending on the people’s reactions. The most popular item calculated was the “Monogram handbag” from Michael Kors’ New York Fashion Week collection.

The present study used three methods to analyze tweets from the 2019 Paris, Milan, New York, and London Fashion Weeks: the social network analysis, which is a method of determining an entire network’s structure based on the relationship between its actors; topical modeling, a statistical analysis method that determines abstract topics contained in groups of texts; and sentiment analysis. A keyword analysis revealed the characteristics of the collections displayed and the attention received by certain celebrities and influencers. Topic analysis indicated the similarities between collections presented by fashion houses within the same topic. A sentiment analysis through WordNet showed the emotional valence of brands that were most frequently mentioned by customers.

The study has three main findings, outlined below. First, a semantic network analysis of the Big 4 Fashion Weeks’ Twitter data indicates that SNS and influencers were referenced more often than brands or designers. The street mood was the most commonly represented fashion sentiment, and leather jackets and faux fur jackets were the most popular fashion items. Interestingly, the textual data from all four cities, especially New York, contained words related to ethical fashion. These results reflect a change in the fashion industry, which had in the past been criticized for causing environmental pollution and animal abuse. The results also show a movement from commercial and practical designs, which were traditionally common in New York’s fashion industry, toward more sustainable designs. Second, a topic modeling based on tweets of Twitter users revealed that the data was best understood when classified into three topics for each city. In addition, most topics were classified based on the sentiment of the collection developed by the brand. Third, to analyze the sentiment of each brand, we used a sentiment analysis divided into three categories (positive, neutral, and negative). It revealed that even the brands that were frequently mentioned in Twitter showed differences in their positive ratings. The most acclaimed brand was Michael Kors from New York Fashion Week, and the fashion item that received the most public attention was the Monogram handbag.

The Paris Fashion Week has developed as a national brand that is contributing to building a positive national image, with support from the French government. In contrast, the New York Fashion Week receives sponsorships from corporations and the New York city. It has also devoted significant resources to marketing, thereby making the New York Fashion Week very corporate and bureaucratic. The Milan Fashion Week is significantly closed off to new and foreign designers and selects designers through internal examinations of their creativity, public relations, sales, and distribution rates. Additionally, several designers who presented their collections during the London Fashion Week left London due to low profitability. Most designers from London have their own small brands; however, some of them work for big fashion houses abroad (Joo, 2016 ). Our study reveals that each city’s fashion week reflects the city’s characteristics. For instance, Paris shows diversity in brands and designer spaces while New York generates popularity by appealing to people’s practical sensibilities.

The public’s interest in celebrities and influencers is a key finding that has various marketing implications. People usually express their desire to become like their role-models by imitating their fashion styles (Lee & Kim, 2019 ). Therefore, celebrities and influencers often create new fashion trends and increase the consumers’ desire to purchase new clothing styles. Our research confirms the influence exerted by various fashion bloggers and social media influencers by the frequency of tweets mentioning them. In fact, except in Paris, these influencers had more influence than brands or designers did. The results, therefore, indicate that fashion brands and designers can popularize their products by leveraging social media influencers. Currently, many fashion brands invite celebrities to advertise their products. However, they should go a step further and analyze the influence of influencers and models on social media to utilize their popularity for marketing purposes.

Theoretical contributions

This study added meaningful insights to the fashion communication literature through an empirical analysis of the Big 4 fashion weeks. This study’s use of text mining, semantic network analysis, topic modeling, and sentiment analysis for quantitatively analyzing the keywords from the social media data about the fashion weeks deepened the discipline of informatics in fashion research. It also proposed ways to minimize the errors caused by subjective interpretation of experts to derive novel and generalized insights from quantitative data.

Additionally, while our findings used similar methods as previous studies (An & Park, 2020 ; Zhao & Min, 2019 ), it added new insights to fashion informatics research by including techniques such as topic modeling and sentiment analysis. From the perspective of fashion research, we proposed a new methodology for analyzing fashion collections by incorporating a qualitative analysis to traditional topic modeling techniques. This study also proposed a new tool to analyze consumers’ evaluation of the fashion brands’ marketing activities, which in turn is based on consumers’ brand sentiments. Traditional approaches for the evaluation of fashion brands using social media big data has been limited to survey methods (Heo & Lee, 2019 ). However, this study’s approach goes beyond surveys and financial statement analysis to provide new academic and managerial implications for evaluating the marketing performance of fashion companies. Therefore, the methods utilized in this study complement that of the previous studies on fashion design and consumer responses in that they successfully identify various design themes and brand evaluations that had not been previously explained.

The impact of SNS and fashion influencers can be explained by the trickle-down theory. Simmel ( 1904 ) theorized that people in the lower socioeconomic classes emulate the clothing and symbology of higher socioeconomic classes to achieve mobility. McCracken ( 1988 ) modified Simmel's point and claimed that rather than rich individuals, powerful and influential people are more likely to be emulated. The original trickle-down theory (Simmel, 1904 ; Veblen, 1899 ) accounts for the importance of pricing and upper classes’ attention to large fashion houses; however, McCracken’s version helps to elucidate the influence of these smaller but more powerful designers on consumer tastes (Walmsely, 2011 ). However, with the development of social media and the emergence of micro consumer segments who follow the influencers’ curated information, merely analyzing visual data is insufficient for explaining street fashion. Therefore, analyzing the communication generated by influencers during the fashion week can help fashion brands to predict the fashion trends with the biggest impact. In this context, this study’s empirical analysis provides meaningful insights on real street fashion.

Practical contributions

Acknowledging that the fashion week plays an important role as a means of communication between brands and customers, analyzing consumer-driven data provides considerable value for fashion marketing and retailing. In this study, we used SNS data to determine popular brands and fashion items and gained more insights on the themes embodied by brands and collections. This method has several practical implications. For example, using our informatics methods, fashion brands and designers can use consumer-driven data to outline their strengths and weaknesses and make improvements for the next fashion week, thereby providing useful information for marketing or product planning in the fashion industry, where fashion trends and inspirations are crucial.

Moreover, each brand’s sentimental evaluation during the fashion week can be numerically cross-checked through consumers’ tweets. Twitter is one of the fastest platforms that relays consumers’ reactions, making it easy to perform numerical calculations of a brand’s sentimental evaluation, especially in comparison with other brands. We classified the consumers' Twitter reactions (through retweets, likes, and quote tweets) into three elaborate sentiment reactions (positive, neutral, and negative) where the sentimental evaluation of brand was measured at the rate of positive reviews. We also considered consumers’ tweets to measure each brand’s sentimental evaluation, making it much more reflective of the consumers’ opinions than the brand’s performance evaluations while avoiding possible biases from individual experts. This is information could help brands adapt and improve their collections for the next fashion week.

Limitations and suggestions for future research

Although the present study offers meaningful additions to the existing literature, it has some limitations that can be addressed through future studies. First, this study’s analytical methods were conducted in English. Therefore, applying the methods used in this study to tweets written in other languages could lead to a lower classification accuracy. Since a fashion week is both a global and a regional event, it would be useful to analyze the trends through the host city’s local language. Second, this study selected the 2019 F/W Fashion Weeks as the target of analysis, which is already outdated in the marketplace. However, the essence of this study is to propose ways to analyze fashion trends through various informative approaches. Since this study analyzed the fashion weeks held in one particular year, additional stop words need to be included in the analysis of fashion weeks in other years or during the Resort/Cruise and Pre-Fall seasons.

This study’s consumer-driven data analysis approach can be repeatedly applied to data from other years (past or future), and it can confirm any long-term changes in other fashion trends. For example, this method can be used for a comparative analysis of fashion trends before and after epidemics such as SARS, MERS, and the Covid-19 pandemic. Furthermore, it can also confirm when the trends analyzed through text mining appear in real street fashion, enriching current studies on fashion trends.

Expanding upon this research, future studies could focus on developing a model that predicts the keywords or popular brands that might be frequently referenced in the fashion weeks. For example, our findings revealed certain noticeable themes such as “sustainability,” “ethical fashion,” and “street style.” Furthermore, while this study focused on the trend analysis, subsequent research can confirm whether certain topics in fashion week are associated with major stream brands’ products and whether it is possible to predict the gatekeeper effect of the fashion weeks. In terms of analytical methods, subsequent studies can investigate how Twitter data can be linked to the trends and patterns in street fashion through the simultaneous use of text mining and regression analysis. Finally, future studies could incorporate a multi-category sentiment analysis to analyze more diverse consumer responses, such as anger, happiness, sadness, and interested/not interested.

This study analyzed and confirmed the city-wise characteristics of the Big 4 Fashion Weeks, the consumer’s perceptions of the designers’ clothes, and the brand’s evaluation. It also contributed to the diversification of fashion research by applying various informatics techniques used in engineering, business, and sociology to analyze fashion collections, and identified ways of applying this methodology to achieve replicable results in the field of fashion Our methodological approach also contributes toward solving the problem of objectivity that was lacking in traditional qualitative analyzes of fashion collections. Consequently, our research’s findings and methods can enable scholars to raise several interesting research questions that can deepen our understanding of the fashion phenomenon in human society. Although the current study itself may be limited in its immediate theoretical implications, its main contribution lies in its ability to stimulate more research that can advance the existing theories in future.

Availability of data and materials

Please contact author for data requests.

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Choi, YH., Yoon, S., Xuan, B. et al. Fashion informatics of the Big 4 Fashion Weeks using topic modeling and sentiment analysis. Fash Text 8 , 33 (2021). https://doi.org/10.1186/s40691-021-00265-6

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Luxury Fashion Branding: Literature Review, Research Trends, and Research Agenda

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I examine in this review paper the literature on luxury fashion branding. I classify the literature into three research types, namely qualitative empirical research, quantitative empirical research, and analytical modeling research. Research scope and core findings on each reviewed literature are presented. Insights on research trends are derived. A few future research areas for scientific studies are identified and formulated as a research agenda. I believe that the research findings can help both practitioners and academicians to better understand the current state of knowledge in terms of academic research on luxury fashion branding. The proposed research agenda, which include topics such as consumer welfare, can help stimulate new applied and basic scientific research on the topic.

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Brand, Consumer and Sustainability Perspectives in Fashion Marketing: Conclusion and Research Agenda

Should the Devil Wear Prada? Analyzing Consumers’ Responses to Luxury Branding

Evolution of Luxury Fashion Brands

Notation for the types of research that are reviewed: [editorial], [qle = qualitative empirical], [qte = quantitative empirical], [am = analytical modeling].

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Systematic Literature Review Of Sustainable Fashion Consumption From 2015 To 2019

This systematic review was designed to assess the previous studies related to consumers’ behaviours towards sustainability in a fashion that covers various fashion items including clothing, jewellery, general products, luxury items, and cosmetics. By following the PRISMA table, the reviewers had initiated this systematic review using the two databases which include Scopus and WOS. All the available articles were selected based on publications from the year 2015 to 2019. After reviewing the keywords, abstracts for the inclusion, exclusion process, and careful reading, a number of 45 papers were selected for reviewing. Further, the reviewer identifies and categories the selected articles based on four main themes of sustainable fashion namely, collaboration, eco-conscious, ethical or slow fashion and recycled and upcycled. The results from this review indicate that there is still a lack of research on sustainable fashion that covers clothing, jewellery and luxury items, as compared to cosmetics and general products such as food and cars. This review provides fruitful directions for future research on sustainable fashion. Keywords: Collaborative fashion eco-conscious fashion ethical/slow fashion recycle/upcycle fashion sustainable fashion

Introduction

In the present century, fashion plays a pivotal role in most places around the world. According to Wiedmann et al. ( 2009 ), people have spent their entire lives by excessively focussing on others’ perceptions about fashion that they wear. The emergence of mass markets and the fluidity of demand have continuously changed this industry to a growing pace. In keeping up with the race in the fashion industry, designers never stop competing to create the next hit as consumers eagerly await the items. Despite providing consumers with the latest styles at low prices, this industry leaves behind a huge negative impact on the planet. The pollution contributed by various chemicals and pesticides used to grow cotton and toxic dyes are accumulating. Apart from the environmental footprints, this industry is also inextricably to gender inequality, labour injustice and poverty issues. The continuity of excessive pollution has in turn ranked this industry as the second biggest contributor of pollution came after oil and gas industry ( Qutab, 2017 ). Currently things seem to be changing. The seriousness of this issue has led fashion to be included in the UN’s 17 Sustainable Development Goals on the agenda to transform the fashion industry by accelerating sustainability with a common framework of the global textile value and exploring many different efforts. Moreover, there is a rise in the number of scholars who claimed that there is a connection between fashion and sustainability ( Amatulli et al., 2018 ). The development of these trends has built a substantial buyer segment, colloquially known as ‘LOHAS’, which can be described as an inclination to practise a healthy and sustainable life ( Emerich, 2012 ). Leveraging on the customer’s interest, more companies are implementing sustainable fashion as the foundation for their businesses.

Problem Statement

Therefore, the motivation to consume sustainable product such as organic food and cosmetic is slightly different with sustainable apparel as the chosen of apparel involved with the development of appearance, self-confidence, style, and image ( Athwal et al., 2019 ). All the product categories should not be treated equally as it will lead them to just claiming their concern about the environment but do not translate this into action ( Scherer et al., 2018 ). Due to that, it is necessary to review the previous studies which had explored the motivational drivers of each sustainable fashion product differently.

Research Questions

The list of research questions derived for this review are:

What is the motivational drivers which triggered the customers to purchase sustainable fashion (based on specific country)?

What are the types of sustainable fashion?

Which sustainable fashion product are still lack of study?

Purpose of the Study

Therefore, this study aims for the first time to shed light on the consumers’ behaviours towards sustainability in fashion by systematically reviewing the literature covering various fashion items including clothing, jewellery, general products, luxury items, and cosmetics. Further, the reviewers had identified and categorised sustainable fashion into four specific main themes. Through the process, this review provides a beneficial in identifying the specific product that is still lacking in research based on different types of sustainable fashion.

After the introduction, the remaining of the paper is constructed as follows: Section 2 is obliged to explain the research methodological in analysing the present state of literature. Section 3 explains the findings matching to the various analysed sub theme of sustainable fashion. Meanwhile, Section 4 discusses the recent consumers’ behaviours towards this trend obtained from different countries. Finally, Section 5 presents the conclusion and future research direction.

Research Methods

This systematic literature review was constructed by the following specific relevant criteria from PRISMA table ( Moher et al., 2009 ) (see Figure 01 ). This method consists of four steps which include identification, screening, eligibility, and inclusion process. The reviewers started gathering the data from January 2019 using two databases, namely WOS and Scopus. In the first step, all keywords and search terms were identified to search related titles and full contents of the journal articles. The three electronic database searches have identified 3113 articles. The next step is the screening process whereby the eligibility and exclusion criteria are determined. The reviewers conducted a record screening and had limited the record search to research published in the last five years, business categories, and articles published in English. Meanwhile, the exclusion process was carried out by excluding journals (systematic review), book series and conference proceedings that are Non-English. The results from the process revealed that 766 articles are considered eligible for a comprehensive review, while a total number of 2339 of the articles have been removed. The third stage is attempting to determine the eligibility process wherein the full articles were retrieved. In sum, a total of 719 articles were eliminated since they were not related to consumer behaviours, decision-making process and the topic of sustainable fashion. A final total number of 45 articles were selected for the review, which consists of 11 qualitative studies, 26 quantitative studies and 8 mixed-method studies.

The findings of the review are presented in a comprehensive four themes form on the different categories of sustainable fashion, including eco-fashion, ethical fashion, collaboration, and recycled/upcycled fashion. These categories of sustainable fashion are also supported by ( Brismar, 2019 ), who had classified sustainable fashion into seven forms (green and clean, custom made and on demand, timeliness design, ethical, upcycle, rent, lease, and second-hand fashion). Shen et al. ( 2013 ) strongly agreed with the statement and stated that from the fashion aspect, all terms labelled with organic, recycled, locally made, fair-trade certified, vegan, custom made and fair-trade certified are categorised as sustainable fashion. In answering all the research questions and knowledge gap, the reviewers had identified previous sustainable fashion studies which not only covered clothing but also include other categories of fashion (jewellery, general products, luxury items and cosmetics).

Further, the analysis results of the review show that the 45 research articles from 24 countries are summarised in Table 01 . This table is organised by including the authors’ names, category of fashion products and sustainable fashion concepts applied.

Figure 02 shows the total number of previous researches related to sustainable fashion products which cover clothing, jewellery and luxury items. The findings demonstrate the lack of research on sustainable fashion which covers clothing, jewellery, and luxury items compared to cosmetics and general products, such as food and cars.

The next section will explain in depth about each fashion item according to the category of sustainable concepts covered in a different country. Finally, the reviewers will provide directions for future research in the perspective of sustainable fashion.

Collaborative fashion

Collaborative fashion can be defined as providing the consumers an option for the alternative of fashion instead of purchasing new fashion products ( Iran et al., 2019 ). There are various alternatives to the classic form of collaboration including swapping, renting and purchasing second-hand fashion. In regards to this systematic review, several scholars had specifically touched on collaboration in terms of clothing and discovered the level of acceptance in China, United States, Iran, and German ( Iran et al., 2019 ; Liang & Xu, 2018 ; Norum & Norton, 2017 ). Based on the research by Iran et al. ( 2019 ), Germany students were more engaged in collaborative fashion compared to the students in Iran. Germany students were more engaged in collaborative fashion compared to the students in Iran. This is due to the cultural differences between German and Iran, whereby German students are found to be shaping more on their feminine culture with power distance and lower uncertainty avoidance rates compared to the Iranian students. Meanwhile, the research conducted in the United States by Norum and Norton ( 2017 ) showed that consumers from Generation Y are labelled as a group who fully practise sustainability in fashion. This is due to the fact that this generation is willing to be entirely involved in second-hand, renting and swapping alternatives compared to Generation X, who are only attracted in consuming second-hand clothing which proves that collaborative is more prevalent and is becoming a new huge business opportunity there ( Liang & Xu, 2018 ).

On top of that, previous studies by Gaur et al. ( 2018 ); McNeill and Venter ( 2019 ) had looked into collaborative fashion as part of the general products in India, the Unites States and New Zealand. The cross-cultural study between United States and India revealed that the customers in United States have more eco-conscious value as compared to the Indian customers ( Gaur et al., 2018 ). The strong argument of difference is that customers from the United States were found to have more harmony orientation towards nature and follow the regulations more strictly. Meanwhile, the involvement of social norms, personal enhancement, and emotion play important roles in influencing the collaborative fashion consumption among the female consumers in New Zealand ( McNeill and Venter, 2019 ). Another research initiative regarding this topic covering the luxury item was conducted by ( Kessous & Valette-Florence, 2019 ; Onel et al. 2018 ; Turunen & Leipämaa-Leskinen, 2015 ). Turunen and Leipämaa-Leskinen ( 2015 ) postulated that the consumption of second-hand product in Finland were driven by five main factors which include sustainable selections, real deals, pre-loved treasures, unique finds and future investment. It can be said that the consumption of luxury second-hand brands is allied to green consciousness, product heritage and symbol of status compared to the first-hand luxury purchase which is more entailed to power-building, social ranking and quality ( Kessous & Valette-Florence, 2019 ). Throughout this systematic review, the reviewers realised that there is an absence of collaborative fashion-related research on jewellery and cosmetics. It may be difficult for both product categories to practice collaboration concepts (swapping, renting and buying second-hand), but it is possible with the availability of social media and online platforms such as Carousell, as it is able to connect the consumers in a wide network while enabling transactions of pre-loved and second-hand products.

Ethical and slow fashion

Ethical aspect has become a prominent concern since the occurrence of excessive consumption, labour abuse and ignorance of animal welfare ( Carrigan et al., 2004 ). On the one hand, the introduction of the slow fashion approach by Godart and Seong ( 2015 ) is synchronised with the movement of sustainability in fashion. Just like slow food, currently, slow fashion is quickly becoming the new black. Based on this systematic literature review, two studies covered the topic of ethics in apparel ( Bly et al., 2015 ; Brandão et al., 2018 ); two studies for jewellery ( Moraes et al., 2017 ; Nash et al., 2016 ); four studies for general products ( Cho et al., 2015 ; Jung & Jin, 2016 ; Lee & Cheon, 2018 ; Ritch, 2015 ); three studies for luxury items ( de Klerk et al., 2019 ; Dong et al., 2018 ; Jung et al., 2016 ) and one study for cosmetics ( Chun, 2016 ). The study conducted by Brandão et al. ( 2018 ) in South Western European countries had exposed that transparency of information does not subsequent a positive significant result. It is shown that retailers need to reduce the creations of the ephemeral fashion and start to implement the concepts of timeliness of style which gives more meaning to customers ( Bly et al., 2015 ). At the same time, de Klerk et al. ( 2019 ), discovered that most customers in South Africa expressed strong ethical concern about luxury products, but never transform it into real action. This is probably due to the excessive material possession and the love of owning luxury products ( Dong et al., 2018 ). Past studies related to cosmetics and personal body care also revealed that the ethical aspect, including honesty, trustworthiness, had left a minimal impact on the consumers’ emotional attachment in the United Kingdom ( Chun, 2016 ). This is due to the customers’’ limited consideration which is only to empathy and citizenship image.

In a different perspective, Nash et al. ( 2016 ) uncovered that over half of the respondents in the United States revealed that ethics-concerned messages are extremely or somewhat important to their jewellery-purchasing decisions. The inclusion of jewellery products with ethical concerns may result in the enhancements in terms of quality, value and uniqueness. Moraes et al. ( 2017 ) stated that to influence customers through ethical practice, there must be a strong linkage between ethical activities and the consumption practices of fine jewellery. Up to this point, the new practices of ethical luxury consumption are likely to succeed with the presence of innovation process involving the convergence of ethical materials and competencies. In terms of general products, customers in the United States were found to purchase ethical brand when they consider the elements of pragmatic benefit and sensory pleasure ( Cho et al., 2015 ; Jung & Jin, 2016 ; Lee & Cheon, 2018 ). The literature on ethical fashion also covers general products specified to food retailing. According to Ritch ( 2015 ), participants in the United Kingdom started to consider ethical production practice as part of their decision-making and supporting market preferences. Overall, previous studies show that ethical and slow fashion provide less and not significant impact on apparel, luxury items and cosmetic products. However, the fashion product categories such as jewellery and general products have a significant impact on the ethical and slow fashion concepts.

Eco-fashion

Environmentally fashion can be described as a fashion involving with an overall process that can maximise the benefits to all wherein minimise the carbon footprint impacts ( Joergens, 2006 ). Based on this systematic review, two studies were focussing on clothing ( Khare & Varshneya, 2017 ; Matthews & Rothenberg, 2017 ); four studies of general product ( Kim et al., 2016 ; Paparoidamis & Tran, 2019 ; Saleem et al., 2018 ; Shin et al., 2018 ); three studies of luxury items ( Ali et al., 2019 ; Fiore et al., 2017 ; Han et al., 2019 ), while nine studies were focussing on cosmetics ( Ahmad & Omar, 2018 ; Amos et al., 2019 ; Baden & Prasad, 2016 ; Chin et al., 2018 ; Ghazali et al., 2017 ; Hsu et al., 2017 ; Kahraman & Kazançoğlu, 2019 ; Ndichu & Upadhyaya, 2019 ; Pudaruth et al., 2015 ;). Past research by Khare and Varshneya ( 2017 ) show that relatives and friends are not important drivers in influencing the organic clothing purchase decisions of youths in India. It can be signifies that a decision to purchase this product is bonded based on personal pro environmental values and previous experience. Besides that, Matthews and Rothenberg ( 2017 ) found that customers in the United States were more concerned about sustainable materials, price and production, rather than the technology adopted. They prefer the eco-friendly labelling over organic labelling for apparels. In addition to that, four studies have discovered the eco-friendly general products. A past study carried out by Saleem et al. ( 2018 ) which explored the relationship between eco-conscious value and social interaction related to the choice and use of eco-personal cars in Pakistan showed a positive significant result. The findings of his study proved that there is a growing of preference towards environmentally friendly product selection in developing countries. Kim et al. (2016) and Paparoidamis and Tran ( 2019 ) share the same findings which revealed that promoting and marketing a new, eco-friendly faux leather product is a worthwhile prospect for the United States and the United Kingdom.

However, another research initiative by Shin et al. ( 2018 ) showed a negative relationship of eco-consciousness among customers in dealing to purchase the detergent‐free washing machines. Moving to green luxury products, several previous studies showed that consumers are started to put their attention on the concept of environmentally friendly and sustainable luxury involving wine, automobile and airplane services ( Ali et al., 2019 ; Fiore et al., 2017 ; Han et al., 2019 ). Most of them are the highly-educated customers and have vast information about environment-related issues. Moving on to organic cosmetics and personal care, a series of past studies exploring this topic, covering several countries, revealed that lifestyles practise, personal image, health and economic conditions are the factors that contribute to these purchasing patterns ( Ahmad & Omar, 2018 ; Amos et al., 2019 ; Chin et al., 2018 ; Ghazali et al., 2017 ; Pudaruth et al., 2015 ). Hsu et al. ( 2017 ) mentioned that the importance of exposing the country of origin, technology in production, and price to influence the eco conscious purchase intention. This review of the current literature reveals that previous scholars have examined numerous studies focussing on the organic cosmetics and environmentally friendly personal care that cover several countries, including the emerging and developing countries. Nevertheless, there is still a lack of studies, especially on the eco-conscious clothing and luxury products in the context of developing countries.

Recycled and upcycled fashion

The raw materials used in producing recycled garments can be attained through the accumulation of supply chain and post-consumer collection methods. It can be manifests that the adoption of recycled materials in a production is synchronise with the global effort to practices the movement to acquire a closed-loop production cycle. Meanwhile, the term “upcycling” was first coined by Kay ( 1994 ) which significantly elucidated the concept of adding value to used and old product, which slightly differs from the concept of recycling that cut the value of the products. Notably, upcycling is synonym to the creation of something new and upgrade the function better from old, used, or disposed items. In sum, the objective of upcycling is to focus on putting a value in a development process that are truly sustainable, creative, and innovative ( Muthu, 2016 ). Previous research has revealed the fact that customers evaluate the products made from recycled plastic positively ( Grönman et al., 2013 ; Magnier et al., 2019 ). Atlason et al. ( 2017 ) found that consumers support the effort to reuse the recycling textile waste to produce new garments and people older than 50 years old with higher education are seem to favour the integration of this sustainable product design. Yet, at the same, the Finnish and Canadian consumers also oftenly separate the basic recycled material (metal, paper, glass) and they practise it in the same ways for their garments collection ( Vehmas et al., 2018 ; Weber et al., 2017 ). As circular clothing is built based on new processes material so customers who avoid to wear second-hand fashion can optionally choose to purchase this circular garments. In part of commercialisation value, circular clothing should be more available on the market while at the same times be branded as luxury items and special editions. However, Rolling and Sadachar ( 2018 ) still argued the effectiveness of using recycled material on luxury items. Their research exposes that despite of putting recycled material as a brand descriptions, it is more effective to use sustainable message element in luxury marketing communication. From the systematic literature review, there are no previous research related to recycle and upcycle fashion implemented in jewellery and cosmetics. It happened due to the irrelevancy of those products to be practiced with this dimension of fashion.

The systematic review provides an insightful information about the consumers’ behaviours towards sustainability in fashion from different countries. At the same frequency, the results of the review were presented in four comprehensive themes based on the category of sustainable fashion which includes collaborative fashion, eco-conscious fashion, ethical fashion, and recycled/upcycled fashion. Taken together, all four dimensions of sustainable fashion are one way to respect the planet and to value what we already have. Despite a wider coverage of sustainable fashion, not everyone is able to practice all the various alternatives mentioned. Consumers have various options to acquire a fashion choice with pro-environmental attributes, so there is no excuse for not practising this clean fashion. It can be seen that sustainable fashion has the big potential to transform the entire fashion industry to positive movement, nevertheless the impact still depends on the prospect customers willingness to pay more for ethically produced clothing ¬and the readiness of brands to sacrifice profits and shift to sustainable. It was found that there is a lack of research in fashion products likes clothing, jewellery and luxury products, compared to cosmetics and general products like food and cars. For further exploration, it would be noteworthy to study this specific category of product. In addition, the future study can be conducted in an experimental study by considering how consumers react to a certain stimulus and all the ways that are able to influence their sustainable fashion consumption.

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Hasbullah, N. N., Sulaiman, Z., & Mas’od, A. (2020). Systematic Literature Review Of Sustainable Fashion Consumption From 2015 To 2019. In Z. Ahmad (Ed.), Progressing Beyond and Better: Leading Businesses for a Sustainable Future, vol 88. European Proceedings of Social and Behavioural Sciences (pp. 341-351). European Publisher. https://doi.org/10.15405/epsbs.2020.10.30

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Fashion in 20th-century literature.

• Cristina Giorcelli Cristina Giorcelli Università degli Studi Roma Tre
• https://doi.org/10.1093/acrefore/9780190201098.013.64
• Published online: 27 February 2017

In the Western world, for centuries, clothes were generally seen as indexes of vanity and seduction, and thus stigmatized. Since the birth of fashion in the second half of the 19th century, however, they have finally come to be regarded as one of the manifestations of a society’s culture, and, as the actual “stuff” of any period’s life, they have gradually figured more prominently in literary works. From modernism to post-modernism, from Blaise Cendrars and F. Scott Fitzgerald to Bret Easton Ellis and William Gibson, fashion and clothes have indeed signified by revealing individualities, suggesting intentions, manifesting a propensity for play and irony, favoring interpersonal encounters, hinting at class and/or gender relations, and showing connections within the social “fabric.” Today, fashion’s prevailing “mix and match” technique—in which references to designers’ own previous creations and to the medium’s past are frequently made—may be inspired or echoed by writers’ ample employment of self-referentiality and intertextuality: in both media attendant discontinuities and aleatory combinations, on the one hand, invite viewers/readers to create their own style/interpretation, and, on the other, establish a diversified continuum, helping to revive the past in new forms.

• fashion theorists
• clothes and exoticism
• clothes and fetishism
• fashion in modernity
• clothes and fashion in post-modernity

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A systematic literature review of modalities, trends, and limitations in emotion recognition, affective computing, and sentiment analysis.

1. Introduction

2. methodology, 2.1. research questions, 2.2. search process, 2.2.1. search terms, 2.2.2. inclusion and exclusion criteria, 2.2.3. quality assessment, 2.2.4. data extraction, 3.1. overview, 3.2. unimodal data approaches, 3.2.1. unimodal physical approaches, 3.2.2. unimodal speech data approaches.

• Several articles mention the use of transfer learning for speech emotion recognition. This technique involves training models on one dataset and applying them to another. This can improve the efficiency of emotion recognition across different datasets.
• Some articles discuss multitask learning models, which are designed to simultaneously learn multiple related tasks. In the context of speech emotion recognition, this approach may help capture commonalities and differences across different datasets or emotions.
• Data augmentation techniques are mentioned in multiple articles, which involve generating additional training data from existing data, which can improve model performance and generalization.
• Attention mechanisms are a common trend for improving emotion recognition. Attention models allow the model to focus on specific features or segments of the input data that are most relevant for recognizing emotions, such as in multi-level attention-based approaches.
• Many articles discuss the use of deep learning models, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and some variants like “Two-Stage Fuzzy Fusion Based-Convolution Neural Network, “Deep Convolutional LSTM”, and “Attention-Oriented Parallel CNN Encoders”.
• While deep learning is prevalent, some articles explore novel feature engineering methods, such as modulation spectral features and wavelet packet information gain entropy, to enhance emotion recognition.
• From the list of articles on unimodal emotion recognition through speech, 7.14% address the challenge of recognizing emotions across different datasets or corpora. This is an important trend for making emotion recognition models more versatile.
• A few articles focus on making emotion recognition models more interpretable and explainable, which is crucial for real-world applications and understanding how the model makes its predictions.
• Ensemble methods, which combine multiple models to make predictions, are mentioned in several articles as a way to improve the performance of emotion recognition systems.
• Some articles discuss emotion recognition in specific contexts, such as call/contact centers, school violence detection, depression detection, analysis of podcast recordings, noisy environment analysis, in-the-wild sentiment analysis, and speech emotion segmentation of vowel-like and non-vowel-like regions. This indicates a trend toward applying emotion recognition in diverse applications.

3.2.3. Unimodal Text Data Approaches

3.2.4. unimodal physiological data approaches.

• Attention and self-attention mechanisms: These suggest that researchers are paying attention to the relevance of different parts of EEG signals for emotion recognition.
• Generative adversarial networks (GANs): Used for generating synthetic EEG data in order to improve the robustness and generalization of the models.
• Semi-supervised learning and domain transfer: Allow emotion recognition with limited datasets or datasets that are applicable to different domains, suggesting a concern for scalability and generalization of models.
• Interpretability and explainability: There is a growing interest in models that are interpretable and explainable, suggesting a concern for understanding how models make decisions and facilitating user trust in them.
• Utilization of transformers and capsule networks: Newer neural network architectures such as transformers and capsule networks are being explored for emotion recognition, indicating an interest in enhancing the modeling and representation capabilities of EEG signals.
• Although studies with a unimodal physical approach using signals different from EEG, like ECG, EDA, HR, and PPG, are still scarce, these can provide information about the cardiovascular system and the body’s autonomic response to emotions. Their limitations are that they may not be as specific or sensitive in detecting subtle or changing emotions. Noise and artifacts, such as motion, can affect the quality of these signals in practical situations and can be influenced by non-emotional factors, such as physical exercise and fatigue. Various studies explore the utilization of ECG and PPG signals for emotion recognition and stress classification. Techniques such as CNNs, LSTMs, attention mechanisms, self-supervised learning, and data augmentation are employed to analyze these signals and extract meaningful features for emotion recognition tasks. Bayesian deep learning frameworks are utilized for probabilistic modeling and uncertainty estimation in emotion prediction from HB data. These approaches aim to enhance human–computer interaction, improve mental health monitoring, and develop personalized systems for emotion recognition based on individual user characteristics.

3.3. Multi-Physical Data Approaches

• Most studies employ CNNs and RNNs, while others utilize variations of general neural networks, such as spiking neural networks (SNN) and tree-based neural networks. SNNs represent and transmit information through discrete bursts of neuronal activity, known as “spikes” or “pulses”, unlike conventional neural networks, which process information in continuous values. Additionally, several studies leverage advanced analysis models such as the stacked ensemble model and multimodal fusion models, which focus on integrating diverse sources of information to enhance decision-making. Transfer learning models and hybrid attention networks aim to capitalize on knowledge from related tasks or domains to improve performance in a target task. Attention-based neural networks prioritize capturing relevant information and patterns within the data. Semi-supervised and contrastive learning models offer alternative learning paradigms by incorporating both labeled and unlabeled data.
• The studies address diverse applications, including sarcasm, sentiment, and emotion recognition in conversations, financial distress prediction, performance evaluation in job interviews, emotion-based location recommendation systems, user experience (UX) analysis, emotion detection in video games, and in educational settings. This suggests that emotion recognition thorough multi-physical data analysis has a wide spectrum of applications in everyday life.
• Various audio and video signal processing techniques are employed, including pitch analysis, facial feature detection, cross-attention, and representational learning.

3.4. Multi-Physiological Data Approaches

• The fusion of physiological signals, such as EEG, ECG, PPG, GSR, EMG, BVP, EOG, respiration, temperature, and movement signals, is a predominant trend in these studies. The combination of multiple physiological signals allows for a richer representation of emotions.
• Most studies apply deep learning models, such as CNNs, RNNs, and autoencoder neural networks (AE), for the processing and analysis of these signals. Supervised and unsupervised learning approaches are also used.
• These studies focus on a variety of applications, such as emotion recognition in healthcare environments, brain–computer interfaces for music, emotion detection in interactive virtual environments, stress assessment in mobility environments for visually impaired people, among others. This indicates that emotion recognition based on physiological signals has applications in healthcare, technology, and beyond.
• Some studies focus on personalized emotion recognition, suggesting tailoring of models for each individual. This may be relevant for personalized health and wellness applications. Others focus on interactive applications and virtual environments useful for entertainment and virtual therapy.
• It is important to mention that the studies within this classification are quite limited in comparison to the previously described modalities. Although it appears that they are using similar physiological signals, the databases differ in terms of their approaches and generation methods. Therefore, there is an opportunity to establish a protocol for generating these databases, allowing for meaningful comparisons among studies.

3.5. Multi-Physical–Physiological Data Approaches

• Studies tend to combine multiple types of signals, such as EEG, facial expressions, voice signals, GSR, and other physiological data. Combining signals aims to take advantage of the complementarity of different modalities to improve accuracy in emotion detection.
• Machine learning models, in particular CNNs, are widely used in signal fusion for emotion recognition. CNN models can effectively process data from multiple modalities.
• Applications are also being explored in the health and wellness domain, such as emotion detection for emotional health analysis of people in smart environments.
• The use of standardized and widely accepted databases is important for comparing results between different studies; however, these are still limited.
• The trend towards non-intrusive sensors and wireless technology enables data collection in more natural and less intrusive environments, which facilitates the practical application of these systems in everyday environments.

4. Discussion

• Facial expression analysis approaches are currently being applied across various domains, including naturalistic settings (“in the wild”), on-road driver monitoring, virtual reality environments, smart homes, IoT and edge devices, and assistive robots. There is also a focus on mental health assessment, including autism, depression, and schizophrenia, and distinguishing between genuine and unfelt facial expressions of emotion. Efforts are being made to improve performance in processing faces acquired at a distance despite the challenges posed by low-quality images. Furthermore, there is an emerging interest in utilizing facial expression analysis in human–computer interaction (HCI), learning environments, and multicultural contexts.
• The recognition of emotions through speech and text has experienced tremendous growth, largely due to the abundance of information facilitated by advancements in technology and social media. This has enabled individuals to express their opinions and sentiments through various media, including podcast recordings, live videos, and readily available data sources such as social media platforms like Twitter, Facebook, Instagram, and blogs. Additionally, researchers have utilized unconventional sources like stock market data and tourism-related reviews. The variety and richness of these data sources indicate a wide range of segments where such emotion recognition analyses can be applied effectively.
• EEG signals continue to be a prominent modality for emotion recognition due to their highly accurate insights into emotional states. Between 2022 and 2023, studies in this field experienced exponential growth. The identified trends include utilizing EEG for enhancing human–computer interaction, recognizing emotions in various contexts such as patients with consciousness disorders, movie viewing, virtual environments, and driving scenarios. EEG is being used for detecting and monitoring mental health issues. There is also a growing focus on personalization, leading towards more individualized and user-specific emotion recognition systems, Other physiological signals, such as ECG, EDA, and HR, are also gaining attention, albeit at a slower pace.
• In the realm of multi-physical, multi-physiological, and multi-physical–physiological approaches, it is the former that appears to be laying the groundwork, as evidenced by the abundance of studies in this area. The latter two approaches, incorporating fusions with physiological signals, are still relatively scarce but seem to be paving the way for future researchers to contribute to their growth. Multimodal approaches, which integrate both physical and physiological signals, are finding diverse applications in emotion recognition. These range from healthcare systems, individual and group mood research, personality recognition, pain intensity recognition, anxiety detection, work stress detection, stress classification and security monitoring in public spaces, to vehicle security monitoring, movie audience emotion recognition, applications for autism spectrum disorder detection, music interfacing, and virtual environments.
• Bidirectional encoder representations from transformers: Used in sentiment analysis and emotion recognition from text, BERT models can understand the context of words in sentences by pre-training on a large text and then fine-tuning for specific tasks like sentiment analysis.
• CNNs: These are commonly applied in facial emotion recognition, emotion recognition from physiological signals, and even in speech emotion recognition by analyzing spectrograms.
• RNNS and variants (LSTM, GRU): These models are suited for sequential data like speech and text. LSTMs and GRUs are particularly effective in speech emotion recognition and sentiment analysis of time-series data.
• Graph convolutional networks (GCNs): Applied in emotion recognition from EEG signals and conversation-based emotion recognition, these can model relational data and capture the complex dependencies in graph-structured data, like brain connectivity patterns or conversational contexts.
• Attention mechanisms and transformers: Enhancing the ability of models to focus on relevant parts of the data, attention mechanisms are integral to models like transformers for tasks that require understanding the context, such as sentiment analysis in long documents or emotion recognition in conversations.
• Ensemble models: Combining predictions from multiple models to improve accuracy, ensemble methods are used in multimodal emotion recognition, where inputs from different modalities (e.g., audio, text, and video) are integrated to make more accurate predictions.
• Autoencoders and generative adversarial networks (GANs): For tasks like data augmentation in emotion recognition from EEG or for generating synthetic data to improve model robustness, these unsupervised learning models can learn compact representations of data or generate new data samples, respectively.
• Multimodal fusion models: In applications requiring the integration of multiple data types (e.g., speech, text, and video for emotion recognition), fusion models combine features from different modalities to capture more comprehensive information for prediction tasks.
• Transfer learning: Utilizing pre-trained models on large datasets and fine-tuning them for specific affective computing tasks, transfer learning is particularly useful in scenarios with limited labeled data, such as sentiment analysis in niche domains.
• Spatio-temporal models: For tasks that involve data with both spatial and temporal dimensions (like video-based emotion recognition or physiological signal analysis), models that capture spatio-temporal dynamics are employed, combining approaches like CNNs for spatial features and RNNs/LSTMs for temporal features.

5. Conclusions

Author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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García-Hernández, R.A.; Luna-García, H.; Celaya-Padilla, J.M.; García-Hernández, A.; Reveles-Gómez, L.C.; Flores-Chaires, L.A.; Delgado-Contreras, J.R.; Rondon, D.; Villalba-Condori, K.O. A Systematic Literature Review of Modalities, Trends, and Limitations in Emotion Recognition, Affective Computing, and Sentiment Analysis. Appl. Sci. 2024 , 14 , 7165. https://doi.org/10.3390/app14167165

García-Hernández RA, Luna-García H, Celaya-Padilla JM, García-Hernández A, Reveles-Gómez LC, Flores-Chaires LA, Delgado-Contreras JR, Rondon D, Villalba-Condori KO. A Systematic Literature Review of Modalities, Trends, and Limitations in Emotion Recognition, Affective Computing, and Sentiment Analysis. Applied Sciences . 2024; 14(16):7165. https://doi.org/10.3390/app14167165

García-Hernández, Rosa A., Huizilopoztli Luna-García, José M. Celaya-Padilla, Alejandra García-Hernández, Luis C. Reveles-Gómez, Luis Alberto Flores-Chaires, J. Ruben Delgado-Contreras, David Rondon, and Klinge O. Villalba-Condori. 2024. "A Systematic Literature Review of Modalities, Trends, and Limitations in Emotion Recognition, Affective Computing, and Sentiment Analysis" Applied Sciences 14, no. 16: 7165. https://doi.org/10.3390/app14167165

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The 6 Biggest Bag Trends of Fall 2024

There’s a lot of color and a little bit of chaos

On the Fall 2024 runways, models wore bags overflowing with stuff like a spare pair of shoes or an extra outfit, as at Miu Miu, or carryalls that were intentionally crinkled, at Prada and Balenciaga. The bags everyone wants right now are the ones that look like they’re living your life with you, and have the marks and personality to prove it.

It is probably the best bag trend to come out of the last couple of seasons, because you’re guaranteed to get your cost-per-wear out of it. The It bags from the Fall/Winter 2024–25 runways aren’t meant to be cycled out after one season—they’re the kind you’ll want to carry over and over and over again.

Ahead, the six biggest bag trends for the upcoming fall season.

Bags in Split-Pea Green

Red continues to dominate the runway as the accent color of choice, but a new shade of green isn’t far behind. In the last couple of seasons, Prada and Miu Miu each offered bags in quirky pea green. It’s not the color you think of when you envision something as serious as a designer carryall, but as seen on the runways at Gucci and Fendi, it is just as energizing as a pop of scarlet or maroon. If anything, it adds a bit of necessary wackiness, making every look feel unexpected in the best possible way.

Shop Green Bags

Gucci Diana Small Tote Bag

Bottega Veneta Mini Arco Tote Bag

JW Anderson Corner Leather Bucket Bag

Miu Miu Nappa Shoulder Bag

Staud Croc-Embossed Leather Shoulder Bag

The return of the bucket bag.

With oversize bags everywhere, micro bags feel like a true relic of runways past. The bucket bag, though—a hot silhouette from the mid-2010s—is making a comeback in the most bite-size proportions. At Ulla Johnson, models dangled suede bucket bags from two fingers as they sauntered down the runway. At Gucci, the trend took a slightly different shape, structured more like a vintage makeup bag. The best thing about the look is how you can’t just toss it over your shoulder—you have to be fully present when wearing it. It’s a bag that demands everyone’s attention, including your own.

Shop Bucket Bags

Reformation Small Silvana Bucket Bag

Saint Laurent Panier Small Leather-Trimmed Raffia Tote

Loewe Mini Pebble Bucket

Prada Suede Mini Bucket Bag

Miu Miu Leather Bucket Bag

Bags made to have and to hold.

At Miu Miu this season, every model looked like the coolest girl you know after she’s raided her even cooler mom’s closet. Pearls were tousled over unbuttoned collars, and carryall bags held with almost comically large gloves. The shape was part bowling bag, part doctor bag, and models clutched them close to their chests, like treasure chests filled with the little pleasures of everyday life.

Shop Carryall Bags

Prada Large Leather Handbag

Alaïa Le Teckel Textured-Leather Shoulder Bag

Métier Perriand City Medium Tote

Savette Symmetry Pochette Leather Tote

Toteme T-Lock Textured-Leather Shoulder Bag

A little ladylike pocketbook.

What happened to the pocketbook? The term is rarely used to describe handbags anymore, because most recent styles don’t possess that dainty, ladylike energy … until now. For her most recent show, Anna Sui sourced vintage tapestry and chenille brocade handbags. At Sandy Liang, models used both of their gloved hands to parade a small bag adorned with a bow ( of course ) down the runway. The polished look is enough to make you yearn for simpler times, when you’d just need to pack a little book instead of your cell phone.

Shop Ladylike Bags

Sandy Liang Secure Bag

Vivienne Westwood Belle Heart Mini Bag

Mango Double Strap Bag

Madewell Medium Crossbody Bag

The Row ’90s Small Leather Shoulder Bag

Bags that make you (literally) feel something.

At Simone Rocha, models carried what appeared to be fluffy stuffed animals, often with beady red eyes. As it turns out, the creatures were modeled after the church grim , a guardian spirit figure of English folklore that is said to protect holy ground from the devil. And while “accessories shaped like children’s toys that are actually inspired by dark lore” was not a trend on the runway, bags with a similar fuzzy texture certainly were. At Prada, they were adorned with bright feathers. At Loewe, the popular squeeze bag was covered in intricate beading, like a bumpy floral portrait on leather. Even at Tory Burch, bags were less sleek than normal, with straps encasing flap bags like a small basket. What everyone wants right now is not just a bag to hold, but one to actually touch, as well.

Shop Textured Bags

Khaite Lilith Faux Fur-Trimmed Leather Tote

Dries Van Noten Leather-Trimmed Tote Bag

Loewe Mini Squeeze Bag in Beaded Leather

PUBLISHED BY Stone Cloud Metallic Shoulder Bag

Blumarine rhinestone-logo faux-fur Tote Bag

Oversize bags overflowing with life.

Oversize bags have been popular now for a few seasons. But how they’re styled has changed during that time. At first, the bags were big, but completely zipped and sealed—they offered a nice, polished solution for carrying everything you need in the course of a typical day. Then there was a noticeable shift at Miu Miu’s Spring 2024 show, where Miuccia Prada found beauty in an overstuffed bag, with a change of clothing and shoes hanging out the side. As I wrote in my review , “Mrs. Prada knows that to be a woman is sometimes to carry a change of outfit in your bag, so you can transform into another version of yourself that you are already expected to be.”

Now, this idea is everywhere. Bags are starting to reflect not a compartmentalization of life but the bustling nature of it. It’s as if they’re giving you permission to be your full, messy self. At Prada, bags had smaller fabric bags tied around the handle. At Tory Burch, straps were left undone, allowing flaps to fling open down the runway. And at Coach, funky personality tchotchkes dangled from purses, loading them up with personality.

Shop Big Bags

Sardine Hobo

Loewe Puzzle Fold XL Convertible Leather Tote

Aventure nappa leather bag

Maison Margiela

Isabel Marant Isabel Marant Wardy Bag Black One Size

Tara Gonzalez is the Senior Fashion Editor at Harper’s Bazaar. Previously, she was the style writer at InStyle , founding commerce editor at Glamour, and fashion editor at Coveteur.

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Research Trends in Virtual Reality Music Concert Technology: A Systematic Literature Review

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Bibliometrics & citations, view options, recommendations, spatial audio in virtual reality: a systematic review.

Spatial Audio represents sound across a full sphere around the listener’s position and is being widely implemented in Virtual Reality and games. However there’s not an extensive body of work in the full benefits of immersive audio in virtual reality, as ...

A Systematic literature review on implementation of virtual reality for learning

Learning in this modern era can use various kinds of technology. One of them is virtual reality. Virtual Reality is an environment generated by a computer that makes the user feel immersed with the object that is generated in their surroundings. ...

Interactive virtual reality orchestral music

The authors developed a VR orchestral application for interactive music experience, allowing virtual musical instruments in an orchestral piece to be repositioned spatially, dynamically and interactively in VR space. This can be done for changing ...

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