{"title":"A Comparison of Dictionary Building Methods for Sentiment Analysis in Software Engineering Text","authors":"M. R. Islam, M. Zibran","doi":"10.1109/ESEM.2017.67","DOIUrl":null,"url":null,"abstract":"Sentiment Analysis (SA) in Software Engineering (SE) texts suffers from low accuracies primarily due to the lack of an effective dictionary. The use of a domain-specific dictionary can improve the accuracy of SA in a particular domain. Building a domain dictionary is not a trivial task. The performance of lexical SA also varies based on the method applied to develop the dictionary. This paper includes a quantitative comparison of four dictionaries representing distinct dictionary building methods to identify which methods have higher/lower potential to perform well in constructing a domain dictionary for SA in SE texts.","PeriodicalId":213866,"journal":{"name":"2017 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)","volume":"213 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"19","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 ACM/IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ESEM.2017.67","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 19
Abstract
Sentiment Analysis (SA) in Software Engineering (SE) texts suffers from low accuracies primarily due to the lack of an effective dictionary. The use of a domain-specific dictionary can improve the accuracy of SA in a particular domain. Building a domain dictionary is not a trivial task. The performance of lexical SA also varies based on the method applied to develop the dictionary. This paper includes a quantitative comparison of four dictionaries representing distinct dictionary building methods to identify which methods have higher/lower potential to perform well in constructing a domain dictionary for SA in SE texts.