期刊文献+

Context-Aware Social Media User Sentiment Analysis 被引量:7

Context-Aware Social Media User Sentiment Analysis
原文传递
导出
摘要 The user-generated social media messages usually contain considerable multimodal content.Such messages are usually short and lack explicit sentiment words.However,we can understand the sentiment associated with such messages by analyzing the context,which is essential to improve the sentiment analysis performance.Unfortunately,majority of the existing studies consider the impact of contextual information based on a single data model.In this study,we propose a novel model for performing context-aware user sentiment analysis.This model involves the semantic correlation of different modalities and the effects of tweet context information.Based on our experimental results obtained using the Twitter dataset,our approach is observed to outperform the other existing methods in analysing user sentiment. The user-generated social media messages usually contain considerable multimodal content.Such messages are usually short and lack explicit sentiment words.However, we can understand the sentiment associated with such messages by analyzing the context, which is essential to improve the sentiment analysis performance.Unfortunately, majority of the existing studies consider the impact of contextual information based on a single data model.In this study, we propose a novel model for performing context-aware user sentiment analysis.This model involves the semantic correlation of different modalities and the effects of tweet context information.Based on our experimental results obtained using the Twitter dataset, our approach is observed to outperform the other existing methods in analysing user sentiment.
出处 《Tsinghua Science and Technology》 SCIE EI CAS CSCD 2020年第4期528-541,共14页 清华大学学报(自然科学版(英文版)
基金 supported by the National Key R&D Program of China(No.2017YFB1003000) the National Natural Science Foundation of China(Nos.61972087and 61772133) the National Social Science Foundation of China(No.19@ZH014) Jiangsu Provincial Key Project(No.BE2018706) the Natural Science Foundation of Jiangsu Province(No.SBK2019022870) Jiangsu Provincial Key Laboratory of Network and Information Security(No.BM2003201) Key Laboratory of Computer Network and Information Integration of Ministry of Education of China(No.93K-9).
关键词 SOCIAL media SENTIMENT analysis MULTIMODAL data CONTEXT-AWARE TOPIC model social media sentiment analysis multimodal data context-aware topic model
  • 相关文献

参考文献1

二级参考文献13

  • 1[1]Turney P D,Littman M L.Measuring praise and criticism:Inference of semantic orientation from association.ACM Transactions on Information Systems,2003,21(4):315 -346.
  • 2[2]Turney P D.Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews.In:Proceeding of Association for Computational Linguistics 40th Anniversary Meeting.Philadelphia:ACU,2002:417-424.
  • 3[3]Chaovalit P,Zhou Lina.Movie review mining:A comparison between supervised and unsupervised classification approaches.In:Proceedings of the 38th Hawaii International Conference on System Sciences.Big Island,Hawaii:IEEE,2005:1 - 9.
  • 4[4]Vapnik V N.The Nature of Statistical Learning Theory.New York:Springer,1998.
  • 5[5]Fei Z C,Liu J,Wu G F.Sentiment classification using phrase patterns.In:Proceedings of the 4th International Conference on Computer and Information Technology (CIT'04).Wuhan,China:IEEE,2004:1-6.
  • 6[6]Pang B,Lee L,Vaithyanathan S.Thumbs up?Sentiment classification using machine learning techniques.In:Proceeding of 2002 Conference on Empirical Methods in Natural Language.Philadelphia:Association for Computational Linguistics,2002:79 - 86.
  • 7[7]Brill E.Some advances in transformation-based part of speech tagging.In:Proceedings of the 12th National Conference on Artificial Intelligence.Menlo Park:AAAI Press,1994:722-727.
  • 8[8]Hatzivassiloglou V,McKeown K R.Predicting the semantic orientation of adjectives.In:Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and the 8th Conference of the European Chapter of the ACL.Madrid:Morgan Kaufmann,1997:174 - 181.
  • 9[9]Turney P D.Mining the Web for synonyms:PMI-IR versus LSA on TOEFL.In:Proceedings of the 12th European Conference on Machine Learning.Berlin:Springer-Verlag,2001:491 - 502.
  • 10[10]Morinaga S,Yamanishi K,Tateishi K,Fukushima T.Mining product reputation on the web.In:Proceeding of K.D.D.2002.Edmonton,Alberta:ACM Press,2002:1 - 8.

共引文献6

同被引文献48

引证文献7

二级引证文献13

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部