隐式特征提取的联合主题-意见模型

2015 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE) Pub Date : 2015-11-01 DOI:10.1109/ISKE.2015.17

Li Sun, Jie Chen, Jiyun Li, YingLi Peng

{"title":"隐式特征提取的联合主题-意见模型","authors":"Li Sun, Jie Chen, Jiyun Li, YingLi Peng","doi":"10.1109/ISKE.2015.17","DOIUrl":null,"url":null,"abstract":"Topic model has been used to extract implicit features yet little concerns have been given to general opinion words, e.g., \"Okey\" (good). In this paper we present a modified topic model joint topic-opinion model (JTO) for extracting implicit features of opinion words including special and general ones. Our model is based on an extension to standard LDA model by adding an opinion level. This model considers both topics and context of opinion words. Experiments show that JTO provides higher accuracy in implicit features extraction.","PeriodicalId":312629,"journal":{"name":"2015 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"11","resultStr":"{\"title\":\"Joint Topic-Opinion Model for Implicit Feature Extracting\",\"authors\":\"Li Sun, Jie Chen, Jiyun Li, YingLi Peng\",\"doi\":\"10.1109/ISKE.2015.17\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Topic model has been used to extract implicit features yet little concerns have been given to general opinion words, e.g., \\\"Okey\\\" (good). In this paper we present a modified topic model joint topic-opinion model (JTO) for extracting implicit features of opinion words including special and general ones. Our model is based on an extension to standard LDA model by adding an opinion level. This model considers both topics and context of opinion words. Experiments show that JTO provides higher accuracy in implicit features extraction.\",\"PeriodicalId\":312629,\"journal\":{\"name\":\"2015 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)\",\"volume\":\"11 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-11-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"11\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2015 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISKE.2015.17\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISKE.2015.17","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 11

摘要

主题模型被用来提取隐式特征，但很少关注一般意见词，例如“Okey”(好)。本文提出了一种改进的主题模型——联合主题-意见模型(JTO)，用于提取意见词的隐式特征，包括特殊意见词和一般意见词。我们的模型是基于对标准LDA模型的扩展，增加了意见级别。该模型同时考虑了意见词的主题和语境。实验表明，JTO在隐式特征提取方面具有较高的精度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

查看原文本刊更多论文

Joint Topic-Opinion Model for Implicit Feature Extracting

Topic model has been used to extract implicit features yet little concerns have been given to general opinion words, e.g., "Okey" (good). In this paper we present a modified topic model joint topic-opinion model (JTO) for extracting implicit features of opinion words including special and general ones. Our model is based on an extension to standard LDA model by adding an opinion level. This model considers both topics and context of opinion words. Experiments show that JTO provides higher accuracy in implicit features extraction.

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

2015 10th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)

自引率

0.00%

发文量