基于潜在Dirichlet分配的阿拉伯语面向领域情感词典构建

Hasan A. Alshahrani, A. Fong
{"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}
引用次数: 3

摘要

情感词汇在情感分析过程中起着至关重要的作用。有效的词典是能够为分类器提供每个类的正确标记(正数和负数)的词典。在本文中,我们使用一种称为潜在狄利克雷分配(Latent Dirichlet Allocation, LDA)的生成统计模型自动构建了一个面向领域的阿拉伯语情感词典。我们通过文档分类来测试我们的词典,并将其与基于先前研究中创建的手动词典所做的分类进行比较。我们在准确率和召回率方面都取得了很好的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信