Thariq M. Jauhari, Soomin Kim, Máté Kovács, U. Serdült, V. Kryssanov
{"title":"Assessing Customer Needs Based On Online Reviews: A Topic Modeling Approach","authors":"Thariq M. Jauhari, Soomin Kim, Máté Kovács, U. Serdült, V. Kryssanov","doi":"10.5167/UZH-188603","DOIUrl":null,"url":null,"abstract":"The fashion industry is one of the most exposed to new online trends manifesting themselves on the internet. Whereas fashion consumers used to get inspired from their preferred brand or print magazine to buy clothes, today, they are rather influenced by social media and online reviews. Online shoppers look for clothes on their own, basing their choices on individual preferences and values. In other words, consumers have become more focused on \"indirect experiences\" and \"exploration\" rather than buying products from specific brands in the store. Furthermore, consumers want to know more about the products, and the fashion market demands greater transparency. From online reviews and ratings, consumers can gather a variety of helpful subjective information from each other. This research is conducted by looking at online product review data from Amazon, one of the leading online shopping websites worldwide, to reveal the hidden topics that are available within the review texts. To do this, topic modeling is applied to the data to explore customer preferences and consumption trends. The results show that the online reviews used in this study can be grouped into four general topics discussed online: Accessories, Outfit, Quality, and Appearance. With this information available, it would benefit and improve fashion businesses in account for product development.","PeriodicalId":405000,"journal":{"name":"International Workshop on Innovations in Information and Communication Science and Technology","volume":"39 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Workshop on Innovations in Information and Communication Science and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5167/UZH-188603","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3
Abstract
The fashion industry is one of the most exposed to new online trends manifesting themselves on the internet. Whereas fashion consumers used to get inspired from their preferred brand or print magazine to buy clothes, today, they are rather influenced by social media and online reviews. Online shoppers look for clothes on their own, basing their choices on individual preferences and values. In other words, consumers have become more focused on "indirect experiences" and "exploration" rather than buying products from specific brands in the store. Furthermore, consumers want to know more about the products, and the fashion market demands greater transparency. From online reviews and ratings, consumers can gather a variety of helpful subjective information from each other. This research is conducted by looking at online product review data from Amazon, one of the leading online shopping websites worldwide, to reveal the hidden topics that are available within the review texts. To do this, topic modeling is applied to the data to explore customer preferences and consumption trends. The results show that the online reviews used in this study can be grouped into four general topics discussed online: Accessories, Outfit, Quality, and Appearance. With this information available, it would benefit and improve fashion businesses in account for product development.