ABSTRACT
The explosion of social media services presents a great opportunity to understand the sentiment of the public via analyzing its large-scale and opinion-rich data. In social media, it is easy to amass vast quantities of unlabeled data, but very costly to obtain sentiment labels, which makes unsupervised sentiment analysis essential for various applications. It is challenging for traditional lexicon-based unsupervised methods due to the fact that expressions in social media are unstructured, informal, and fast-evolving. Emoticons and product ratings are examples of emotional signals that are associated with sentiments expressed in posts or words. Inspired by the wide availability of emotional signals in social media, we propose to study the problem of unsupervised sentiment analysis with emotional signals. In particular, we investigate whether the signals can potentially help sentiment analysis by providing a unified way to model two main categories of emotional signals, i.e., emotion indication and emotion correlation. We further incorporate the signals into an unsupervised learning framework for sentiment analysis. In the experiment, we compare the proposed framework with the state-of-the-art methods on two Twitter datasets and empirically evaluate our proposed framework to gain a deep understanding of the effects of emotional signals.
- R. Abelson. Whatever became of consistency theory? Personality and Social Psychology Bulletin, 1983.Google ScholarCross Ref
- A. Andreevskaia and S. Bergler. Mining wordnet for fuzzy sentiment: Sentiment tag extraction from wordnet glosses. In Proceedings of EACL, 2006.Google Scholar
- S. Asur and B. Huberman. Predicting the future with social media. In WI-IAT, pages 492--499, 2010. Google ScholarDigital Library
- J. Bollen, H. Mao, and X. Zeng. Twitter mood predicts the stock market. Journal of Computational Science, 2011.Google ScholarCross Ref
- S. Boyd and L. Vandenberghe. Convex optimization. Cambridge university press, 2004. Google ScholarDigital Library
- S. Brody and N. Diakopoulos. Cooooooooooooooollllllllllllll!!!!!!!!!!!!!!: using word lengthening to detect sentiment in microblogs. In Proceedings of EMNLP, pages 562--570, 2011. Google ScholarDigital Library
- C. Ding, T. Li, and M. Jordan. Convex and semi-nonnegative matrix factorizations. IEEE TPAMI, 32:45--55, 2010. Google ScholarDigital Library
- C. Ding, T. Li, W. Peng, and H. Park. Orthogonal nonnegative matrix t-factorizations for clustering. In Proceedings of SIGKDD, pages 126--135, 2006. Google ScholarDigital Library
- T. Egener, J. Granado, and M. Guitton. High frequency of phenotypic deviations in physcomitrella patens plants transformed with a gene-disruption library. BMC Plant Biology, 2:6, 2002.Google ScholarCross Ref
- A. Go, R. Bhayani, and L. Huang. Twitter sentiment classification using distant supervision. Technical Report, Stanford, pages 1--12, 2009.Google Scholar
- Q. Gu and J. Zhou. Co-clustering on manifolds. In Proceedings of SIGKDD, pages 359--368, 2009. Google ScholarDigital Library
- T. Hofmann. Probabilistic latent semantic indexing. In Proceedings of SIGIR, pages 50--57, 1999. Google ScholarDigital Library
- M. Hu and B. Liu. Mining and summarizing customer reviews. In Proceedings of SIGKDD, 2004. Google ScholarDigital Library
- X. Hu, N. Sun, C. Zhang, and T.-S. Chua. Exploiting internal and external semantics for the clustering of short texts using world knowledge. In Proceedings of CIKM, pages 919--928, 2009. Google ScholarDigital Library
- X. Hu, L. Tang, J. Tang, and H. Liu. Exploiting social relations for sentiment analysis in microblogging. In Proceedings of WSDM, 2013. Google ScholarDigital Library
- Y. Hu, S. D. Farnham, and A. Monroy-Hernández. Whoo. ly: Facilitating information seeking for hyperlocal communities using social media. In Proceedings of CHI, 2013. Google ScholarDigital Library
- Y. Hu, A. John, D. Seligmann, and F. Wang. What were the tweets about? topical associations between public events and twitter feeds. ICWSM, 2012.Google Scholar
- E. Kim, S. Gilbert, M. Edwards, and E. Graeff. Detecting sadness in 140 characters: Sentiment analysis of mourning michael jackson on twitter. 2009.Google Scholar
- T. Li, V. Sindhwani, C. Ding, and Y. Zhang. Bridging domains with words: Opinion analysis with matrix tri-factorizations. In Proceedings of SDM, 2010.Google ScholarCross Ref
- B. Liu. Sentiment analysis and subjectivity. Handbook of Natural Language Processing, 2010.Google Scholar
- B. Liu and L. Zhang. A survey of opinion mining and sentiment analysis. Mining Text Data, 2012.Google ScholarCross Ref
- K.-L. Liu, W.-J. Li, and M. Guo. Emoticon smoothed language models for twitter sentiment analysis. In Proceedings of AAAI, 2012.Google Scholar
- Y. Lu, M. Castellanos, U. Dayal, and C. Zhai. Automatic construction of a context-aware sentiment lexicon: an optimization approach. In Proceedings of WWW, pages 347--356, 2011. Google ScholarDigital Library
- B. O Connor, R. Balasubramanyan, B. Routledge, and N. Smith. From tweets to polls: Linking text sentiment to public opinion time series. In Proceedings of ICWSM, 2010.Google Scholar
- B. Pang, L. Lee, and S. Vaithyanathan. Thumbs up?: sentiment classification using machine learning techniques. In Proceedings of ACL, pages 79--86, 2002. Google ScholarDigital Library
- W. Peng and D. H. Park. Generate adjective sentiment dictionary for social media sentiment analysis using constrained nonnegative matrix factorization. In ICWSM, 2011.Google Scholar
- S. Prentice and E. Huffman. Social medias new role in emergency management. Idaho National Laboratory, pages 1--5, 2008.Google Scholar
- D. Seung and L. Lee. Algorithms for non-negative matrix factorization. NIPS, pages 556--562, 2001.Google Scholar
- D. Shamma, L. Kennedy, and E. Churchill. Tweet the debates: understanding community annotation of uncollected sources. In Proceedings of WSM, 2009. Google ScholarDigital Library
- M. Speriosu, N. Sudan, S. Upadhyay, and J. Baldridge. Twitter polarity classification with label propagation over lexical links and the follower graph. In Proceedings of the First Workshop on Unsupervised Learning in NLP, 2011. Google ScholarDigital Library
- P. Stone, D. Dunphy, and M. Smith. The general inquirer: A computer approach to content analysis. 1966.Google Scholar
- M. Taboada, J. Brooke, M. Tofiloski, K. Voll, and M. Stede. Lexicon-based methods for sentiment analysis. Computational Linguistics, 2011. Google ScholarDigital Library
- J. Tang, H. Gao, X. Hu, and H. Liu. Exploiting homophily effect for trust prediction. In WSDM, 2013. Google ScholarDigital Library
- P. Turney. Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In Proceedings of ACL, pages 417--424, 2002. Google ScholarDigital Library
- H. Wang, Y. Lu, and C. Zhai. Latent aspect rating analysis on review text data: a rating regression approach. In Proceedings of SIGKDD, 2010. Google ScholarDigital Library
- J. Wiebe, T. Wilson, and C. Cardie. Annotating expressions of opinions and emotions in language. Language Resources and Evaluation, 39:165--210, 2005.Google ScholarCross Ref
- T. Wilson, J. Wiebe, and P. Hoffmann. Recognizing contextual polarity in phrase-level sentiment analysis. In Proceedings of HLT and EMNLP, 2005. Google ScholarDigital Library
- Y. Xie, Z. Chen, K. Zhang, M. M. A. Patwary, Y. Cheng, H. Liu, A. Agrawal, and A. Choudhary. Graphical modeling of macro behavioral targeting in social networks. In Proceedings of SDM, 2013.Google ScholarCross Ref
- L. Zhang and B. Liu. Identifying noun product features that imply opinions. In Proceedings of ACL:HLT, pages 575--580, 2011. Google ScholarDigital Library
- J. Zhao, L. Dong, J. Wu, and K. Xu. Moodlens: an emoticon-based sentiment analysis system for chinese tweets. In Proceedings of SIGKDD, 2012. Google ScholarDigital Library
Index Terms
- Unsupervised sentiment analysis with emotional signals
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