{"title":"基于潜在Dirichlet分配的阿拉伯语面向领域情感词典构建","authors":"Hasan A. Alshahrani, A. Fong","doi":"10.1109/EIT.2018.8500193","DOIUrl":null,"url":null,"abstract":"Sentiment lexicon is crucial in the process of sentiment analysis. The efficient lexicon is the one that is able to provide the classifier with the right tokens of each class, positive and negative. In this paper, we have built a domain-oriented Arabic sentiment lexicon automatically using a generative statistical model called Latent Dirichlet Allocation (LDA). We tested our lexicon by doing documents classification and compare it with a classification done based on a manual lexicon created in previous study. We achieved good results from both accuracy and recall perspectives.","PeriodicalId":188414,"journal":{"name":"2018 IEEE International Conference on Electro/Information Technology (EIT)","volume":"243 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Arabic Domain-Oriented Sentiment Lexicon Construction Using Latent Dirichlet Allocation\",\"authors\":\"Hasan A. Alshahrani, A. Fong\",\"doi\":\"10.1109/EIT.2018.8500193\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Sentiment lexicon is crucial in the process of sentiment analysis. The efficient lexicon is the one that is able to provide the classifier with the right tokens of each class, positive and negative. In this paper, we have built a domain-oriented Arabic sentiment lexicon automatically using a generative statistical model called Latent Dirichlet Allocation (LDA). We tested our lexicon by doing documents classification and compare it with a classification done based on a manual lexicon created in previous study. We achieved good results from both accuracy and recall perspectives.\",\"PeriodicalId\":188414,\"journal\":{\"name\":\"2018 IEEE International Conference on Electro/Information Technology (EIT)\",\"volume\":\"243 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2018-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2018 IEEE International Conference on Electro/Information Technology (EIT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EIT.2018.8500193\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2018 IEEE International Conference on Electro/Information Technology (EIT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EIT.2018.8500193","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Arabic Domain-Oriented Sentiment Lexicon Construction Using Latent Dirichlet Allocation
Sentiment lexicon is crucial in the process of sentiment analysis. The efficient lexicon is the one that is able to provide the classifier with the right tokens of each class, positive and negative. In this paper, we have built a domain-oriented Arabic sentiment lexicon automatically using a generative statistical model called Latent Dirichlet Allocation (LDA). We tested our lexicon by doing documents classification and compare it with a classification done based on a manual lexicon created in previous study. We achieved good results from both accuracy and recall perspectives.