{"title":"Sentiment analysis for bank service quality: A rule-based classifier","authors":"Yuliya Bidulya, Elena G. Brunova","doi":"10.1109/ICAICT.2016.7991688","DOIUrl":null,"url":null,"abstract":"The paper considers the analysis of the subjective information from user-generated content. The purpose of this research is to develop a rule-based classifier for the sentiment analysis within the bank service quality domain. Our sentiment lexicon includes 286 positive and 385 negative words. Besides, three more lexicon classes are added; they are required to apply the rule-based algorithm. To test the algorithm, 200 reviews in Russian are analyzed. The experiment demonstrates that the efficiency of the rule-based classifier is higher as compared to the Naïve Bayes classifier. It is determined that the system generally detects positive reviews better than negative ones.","PeriodicalId":446472,"journal":{"name":"2016 IEEE 10th International Conference on Application of Information and Communication Technologies (AICT)","volume":"1651 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"12","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 IEEE 10th International Conference on Application of Information and Communication Technologies (AICT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICAICT.2016.7991688","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 12
Abstract
The paper considers the analysis of the subjective information from user-generated content. The purpose of this research is to develop a rule-based classifier for the sentiment analysis within the bank service quality domain. Our sentiment lexicon includes 286 positive and 385 negative words. Besides, three more lexicon classes are added; they are required to apply the rule-based algorithm. To test the algorithm, 200 reviews in Russian are analyzed. The experiment demonstrates that the efficiency of the rule-based classifier is higher as compared to the Naïve Bayes classifier. It is determined that the system generally detects positive reviews better than negative ones.
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