Chenhao Tan
Lillian Lee
ABSTRACT
We show that information about social relationships can be used to improve user-level sentiment analysis. The main motivation behind our approach is that users that are somehow "connected" may be more likely to hold similar opinions; therefore, relationship information can complement what we can extract about a user's viewpoints from their utterances. Employing Twitter as a source for our experimental data, and working within a semi-supervised framework, we propose models that are induced either from the Twitter follower/followee network or from the network in Twitter formed by users referring to each other using "@" mentions. Our transductive learning results reveal that incorporating social-network information can indeed lead to statistically significant sentiment classification improvements over the performance of an approach based on Support Vector Machines having access only to textual features.
- A. Agarwal and P. Bhattacharyya. Sentiment analysis: A new approach for effective use of linguistic knowledge and exploiting similarities in a set of documents to be classified. In ICON, 2005.Google Scholar
- R. Agrawal, S. Rajagopalan, R. Srikant, and Y. Xu. Mining newsgroups using networks arising from social behavior. In WWW, pages 529--535, 2003. Google ScholarDigital Library
- L. Barbosa and J. Feng. Robust sentiment detection on twitter from biased and noisy data. In COLING, 2010. Google ScholarDigital Library
- D. Davidov, O. Tsur, and A. Rappoport. Enhanced sentiment learning using twitter hashtags and smileys. In COLING, 2010. Google ScholarDigital Library
- A. B. Goldberg and J. Zhu. Seeing stars when there aren't many stars: Graph-based semi-supervised learning for sentiment categorization. In TextGraphs: HLT/NAACL Wksp. on Graph-based Algorithms for Natural Language Processing, 2006. Google ScholarDigital Library
- A. B. Goldberg, J. Zhu, and S. Wright. Dissimilarity in graph-based semi-supervised classification. In AISTATS, 2007.Google Scholar
- W. Gryc and K. Moilanen. Leveraging textual sentiment analysis with social network modelling: Sentiment analysis of political blogs in the 2008 U.S. presidential election. In Proceedings of the "From Text to Political Positions" Workshop, 2010.Google Scholar
- L. Jiang, M. Yu, M. Zhou, X. Liu, and T. Zhao. Target-dependent twitter sentiment classification. In ACL, 2011. Google ScholarDigital Library
- P. F. Lazarsfeld and R. K. Merton. Friendship as a social process: A substantive and methodological analysis. In M. Berger, T. Abel, and C. H. Page, editors, Freedom and Control in Modern Society, pages 8--66. New York: Van Nostrand, 1954.Google Scholar
- G. Li, S. C. Hoi, K. Chang, and R. Jain. Micro-blogging sentiment detection by collaborative online learning. ICDM, 0: 893--898, 2010. Google ScholarDigital Library
- M. McPherson, L. Smith-Lovin, and J. M. Cook. Birds of a feather: Homophily in social networks. Annual Review of Sociology, 27: 415--444, 2001.Google ScholarCross Ref
- T. Mullen and R. Malouf. Taking sides: User classification for informal online political discourse. Internet Research, 18: 177--190, 2008.Google ScholarCross Ref
- B. O'Connor, R. Balasubramanyan, B. R. Routledge, and N. A. Smith. From tweets to polls: Linking text sentiment to public opinion time series. In ICWSM, 2010.Google ScholarCross Ref
- B. Pang and L. Lee. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In ACL, pages 271--278, 2004. Google ScholarDigital Library
- B. Pang and L. Lee. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In ACL, pages 115--124, 2005. Google ScholarDigital Library
- B. Pang and L. Lee. Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval, 2 (1--2): 1--135, 1 2008. Google ScholarDigital Library
- J. Read. Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In ACL Student Research Wksp., 2005. Google ScholarDigital Library
- V. Sindhwani and P. Melville. Document-word co-regularization for semi-supervised sentiment analysis. In Proceedings of ICDM, pages 1025--1030, 2008. Google ScholarDigital Library
- S. Singh, L. Yao, S. Riedel, and A. McCallum. Constraint-driven rank-based learning for information extraction. In HLT, pages 729--732, 2010. Google ScholarDigital Library
- A. Smith. The Internet and Campaign 2010. http://www.pewinternet.org/Reports/2011/The-Internet-and-Campaign-2010.aspx, 2011.Google Scholar
- S. Somasundaran, G. Namata, L. Getoor, and J. Wiebe. Opinion graphs for polarity and discourse classification. In 2009 Wksp. on Graph-based Methods for Natural Language Processing, pages 66--74, 2009. Google ScholarDigital Library
- H. Tanev, B. Pouliquen, V. Zavarella, and R. Steinberger. Automatic expansion of a social network using sentiment analysis. In Data Mining for Social Network Data, volume 12 of phAnnals of Information Systems, pages 9--29. Springer US, 2010.Google Scholar
- M. Thelwall. Emotion homophily in social network site messages. First Monday, 15 (4--5), 2010.Google ScholarCross Ref
- M. Thomas, B. Pang, and L. Lee. Get out the vote: Determining support or opposition from Congressional floor-debate transcripts. In EMNLP, pages 327--335, 2006. Google ScholarDigital Library
- M. Wick, K. Rohanimanesh, A. Culotta, and A. McCallum. Samplerank: Learning preferences from atomic gradients. In NIPS Wksp. on Advances in Ranking, 2009.Google Scholar
- Q. Wu, S. Tan, and X. Cheng. Graph ranking for sentiment transfer. In Proceedings of the ACL-IJCNLP 2009 Conference Short Papers, pages 317--320, 2009. Google ScholarDigital Library
- B. Yu, S. Kaufmann, and D. Diermeier. Classifying party affiliation from political speech. Journal of Information Technology & Politics, 5 (1): 33--48, 2008.Google ScholarCross Ref
Index Terms
- User-level sentiment analysis incorporating social networks
Recommendations
Exploiting social relations for sentiment analysis in microblogging
WSDM '13: Proceedings of the sixth ACM international conference on Web search and data miningMicroblogging, like Twitter and Sina Weibo, has become a popular platform of human expressions, through which users can easily produce content on breaking news, public events, or products. The massive amount of microblogging data is a useful and timely ...
Social sentiment sensor: a visualization system for topic detection and topic sentiment analysis on microblog
As a new form of social media, microblogging provides platform sharing, wherein users can share their feelings and ideas on certain topics. Bursty topics from microblogs are the results of the emerging issues that instantly attract more followers and ...
Opinion Mining and Sentiment Analysis in Social Networks: A Retweeting Structure-Aware Approach
UCC '14: Proceedings of the 2014 IEEE/ACM 7th International Conference on Utility and Cloud ComputingMicroblogs have become quick and easy online information sharing platforms with the explosive growth of online social media. Weibo, a Twitter-like microblog service in China, is characterized by timeliness and interactivity. A Weibo message carries the ...
Comments