{"title":"Use of Sentiment Analysis for Predicting Public Opinion on Referendum: A Feasibility Study","authors":"Iana Sabatovych","doi":"10.1080/02763877.2019.1595260","DOIUrl":null,"url":null,"abstract":"ABSTRACT This study explored the potential of using sentiment analysis of tweets to predict referendum choices (Brexit). The feasibility of using StreamKM++ in the massive online analysis framework was examined over five categories, ranging from strongly agree to strongly disagree (to exit). A Naïve Bayes classifier was used to classify people’s opinions according to these categories. The prediction model resulted in high accuracy (97.98%), making it possible to use it in predicting opinions about public events and issues. The findings from this study may help practitioners, and policymakers understand the importance of sentiment analysis of social media in assessing public opinion and, accordingly, making certain voting predictions.","PeriodicalId":35386,"journal":{"name":"Reference Librarian","volume":"60 1","pages":"202 - 211"},"PeriodicalIF":0.0000,"publicationDate":"2019-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/02763877.2019.1595260","citationCount":"4","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Reference Librarian","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/02763877.2019.1595260","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Social Sciences","Score":null,"Total":0}
引用次数: 4
Abstract
ABSTRACT This study explored the potential of using sentiment analysis of tweets to predict referendum choices (Brexit). The feasibility of using StreamKM++ in the massive online analysis framework was examined over five categories, ranging from strongly agree to strongly disagree (to exit). A Naïve Bayes classifier was used to classify people’s opinions according to these categories. The prediction model resulted in high accuracy (97.98%), making it possible to use it in predicting opinions about public events and issues. The findings from this study may help practitioners, and policymakers understand the importance of sentiment analysis of social media in assessing public opinion and, accordingly, making certain voting predictions.
期刊介绍：
The Reference Librarian aims to be a standard resource for everyone interested in the practice of reference work, from library and information science students to practicing reference librarians and full-time researchers. It enables readers to keep up with the changing face of reference, presenting new ideas for consideration. The Reference Librarian publishes articles about all aspects of the reference process, some research-based and some applied. Current trends and traditional questions are equally welcome. Many articles concern new electronic tools and resources, best practices in instruction and reference service, analysis of marketing of services, and effectiveness studies.
京公网安备 11010802042870号
京ICP备2023020795号-1
分享
求助内容：
应助结果提醒方式：
扫码关注我们
