{"title":"软件工程文本情感分析的词典构建方法比较","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":"{\"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}","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}
A Comparison of Dictionary Building Methods for Sentiment Analysis in Software Engineering Text
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.