{"title":"Data and text mining from online reviews: An automatic literature analysis","authors":"Sérgio Moro, P. Rita","doi":"10.1002/widm.1448","DOIUrl":null,"url":null,"abstract":"This paper reports on a thorough analysis of the scientific literature using data and text mining to uncover knowledge from online reviews due to their importance as user‐generated content. In this context, more than 12,000 papers were extracted from publications indexed in the Scopus database within the last 15 years. Regarding the type of data, most previous studies focused on qualitative textual data to perform their analysis, with fewer looking for quantitative scores and/or characterizing reviewer profiles. In terms of application domains, information management and technology, e‐commerce, and tourism stand out. It is also clear that other areas of potentially valuable applications should be addressed in future research, such as arts and education, as well as more interdisciplinary approaches, namely in the spectrum of the social sciences.","PeriodicalId":48970,"journal":{"name":"Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery","volume":"5 1","pages":""},"PeriodicalIF":6.4000,"publicationDate":"2022-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1002/widm.1448","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 3
Abstract
This paper reports on a thorough analysis of the scientific literature using data and text mining to uncover knowledge from online reviews due to their importance as user‐generated content. In this context, more than 12,000 papers were extracted from publications indexed in the Scopus database within the last 15 years. Regarding the type of data, most previous studies focused on qualitative textual data to perform their analysis, with fewer looking for quantitative scores and/or characterizing reviewer profiles. In terms of application domains, information management and technology, e‐commerce, and tourism stand out. It is also clear that other areas of potentially valuable applications should be addressed in future research, such as arts and education, as well as more interdisciplinary approaches, namely in the spectrum of the social sciences.
期刊介绍：
The goals of Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery (WIREs DMKD） are multifaceted. Firstly, the journal aims to provide a comprehensive overview of the current state of data mining and knowledge discovery by featuring ongoing reviews authored by leading researchers. Secondly, it seeks to highlight the interdisciplinary nature of the field by presenting articles from diverse perspectives, covering various application areas such as technology, business, healthcare, education, government, society, and culture. Thirdly, WIREs DMKD endeavors to keep pace with the rapid advancements in data mining and knowledge discovery through regular content updates. Lastly, the journal strives to promote active engagement in the field by presenting its accomplishments and challenges in an accessible manner to a broad audience. The content of WIREs DMKD is intended to benefit upper-level undergraduate and postgraduate students, teaching and research professors in academic programs, as well as scientists and research managers in industry.
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