AUTHORS: Jiaqi Wang
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ABSTRACT: Inspired by the principle of Homophily which suggests that opinions are influenced by connection, we introduce relations into sentiment analysis in the context of social networks, which also helps to reduce the content sparsity by utilizing the networked SNS data. We propose a model which utilized textual content and link structure simultaneously to evaluate pair-wise social influence on topic level between users. The framework depicts the topic distribution for each user by LDA based on text information; and model the pair-wise influence between users on topic level by measuring their centralities and interactive weights. The learned influence is then applied into sentiment classification as supplementary features. The experiment results on two datasets show that the model incorporating user relations outperforms the methods which based on textual features only
KEYWORDS: Sentiment Analysis, Social Network, LDA, Topic Model
REFERENCES:
[1] Miller McPherson, Lynn Smith-Lovin and James M Cook, Birds of a feather: Homophily in social networks, Annual review of sociology, 2001, pp. 415–444.
[2] Mike Thelwall, Emotion homophily in social net-work site messages, First Monday, 4, 2010.
[3] Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd, The pagerank citation ranking: bringing order to the web, Technical report, Stan-ford Digital Library Technologies Project, 1999.
[4] Jianshu Weng, Ee-Peng Lim, Jing Jiang, and Qi He, Twitterrank: finding topic-sensitive influential twitterers, Proceedings of the third ACM international conference on Web search and data mining, 2010, pp. 261–270.
[5] Jing Zhang, Biao Liu, Jie Tang, Ting Chen, and Juanzi Li, Social influence locality for modeling retweeting behaviors, Proceedings of the Twenty-Third international joint conference on Artificial Intelligence, AAAI Press, 2013, pp. 2761–2767.
[6] Lu Liu, Jie Tang, Jiawei Han, Meng Jiang, and Shiqiang Yang, Mining topic-level influence in heterogeneous networks, Proceedings of the 19th ACM international conference on Information and knowledge management, 2010, pp. 199–208.
[7] Taher H Haveliwala, Topic-sensitive pagerank, Proceedings of the 11th international conference on World Wide Web, 2002, pp. 517–526.
[8] Jie Tang, Jimeng Sun, Chi Wang, and Zi Yang, Social influence analysis in large-scale networks, Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, 2009, pp. 807–816.
[9] Chenhao Tan, Lillian Lee, Jie Tang, Long Jiang, Ming Zhou, and Ping Li, User-level sentiment analysis incorporating social networks, Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, 2011, pp. 1397–1405.
[10] Bin Bi, Yuanyuan Tian, Yannis Sismanis, Andrey Balmin and Junghoo Cho, ”Scalable topicspecific influence analysis on microblogs, Proceedings of the 7th ACM international conference on Web search and data mining, 2014, pp. 513–522.
[11] Elaine Hatfield, John T Cacioppo, and Richard L Rapson, Emotional contagion, Cambridge university press, 1994.
[12] David M Blei, Andrew Y Ng, and Michael I Jordan, Latent dirichlet allocation, the Journal of machine learning research, 3, 2003, pp. 993– 1022.
[13] Thomas K Landauer, Peter W Foltz, and Darrell Laham, An introduction to latent semantic analysis, Discourse processes, 25, 1998, pp. 259– 284.
[14] Thomas Hofmann, Probabilistic latent semantic indexing, Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, 1999, pp. 50–57.
[15] David M Blei, Probabilistic topic models, Communications of the ACM, 55, 2012, pp. 77–84.
[16] Thomas L Griffiths and Mark Steyvers, Finding scientific topics, Proceedings of the National Academy of Sciences, 101, 2004, pp. 5228–5235.
[17] Gregor Heinrich, Parameter estimation for text analysis, Technical report, 2005.
[18] Tom Griffiths, Gibbs sampling in the generative model of latent dirichlet allocation, 2002.
[19] GW Peters and SA Sisson, Bayesian inference, monte carlo sampling and operational risk, Journal of Operational Risk, 1, 2006, pp. 27–50.
[20] Walter R Gilks, Sylvia Richardson, and David JSpiegelhalter, Introducing markov chain monte carlo, Markov chain Monte Carlo in practice, 1996, pp. 1–19.
