Part of
From Text to Political Positions: Text analysis across disciplinesEdited by Bertie Kaal, Isa Maks and Annemarie van Elfrinkhof
[Discourse Approaches to Politics, Society and Culture 55] 2014
► pp. 47–70
Edited by Bertie Kaal, Isa Maks and Annemarie van Elfrinkhof
[Discourse Approaches to Politics, Society and Culture 55] 2014
► pp. 47–70
Leveraging Textual Sentiment Analysis with Social Network Modelling
Sentiment Analysis of Political Blogs in the 2008 U.S. Presidential Election
Automatic computational analysis of political texts poses major challenges for state-of-the-art Sentiment Analysis and Natural Language Processing tools. In this initial study, we investigate the feasibility of combining purely linguistic indicators of political sentiment with non-linguistic evidence gained from concomitant social network analysis. The analysis draws on a corpus of 2.8 million political blog posts by 16,741 bloggers. We focus on modeling blogosphere sentiment centered around Barack Obama during the 2008 U.S. presidential election, and describe a series of initial sentiment classification experiments on a data set of 700 crowd-sourced posts labeled for attitude with respect to Obama. Our approach employs a hybrid machine-learning and logic-based framework which operates along three distinct levels of analysis encompassing standard shallow document classification, deep linguistic multi-entity sentiment analysis and scoring and social network modeling. The initial results highlight the inherent complexity of the classification task and point towards the positive effects of learning features that exploit entity-level sentiment and social-network structure.
References (28)
Adamic, L. and N. Glance. 2005. The political blogosphere and the 2004 US election: divided they blog. In Proceedings of the 3rd International Workshop on Link Discovery (LinkKDD 2005, New York, NY), pp. 36–43.
Clauset, A., M.E.J. Newman and C. Moore. 2004. Finding community structure in very large networks. Physical Review E, 70(6), 06611, 6 pages. 
Csa´rdi, G. and T. Nepusz. 2006. The igraph software package for complex network research. International Journal Complex Systems, 1695.
Durant, K.T. and M.D. Smith. 2006. Mining sentiment classification from political web logs. In Pro-ceedings of Workshop on Web Mining and Web Usage Analysis of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (WebKDD-2006).
. 2007. Predicting the political sentiment of web log posts using supervised machine learning techniques coupled with feature selection. In Advances in Web Mining and Web Usage Analysis
: Proceedings of the 8th International Workshop on Knowledge Discovery on the Web (WEBKDD 2006, Philadelphia, PA), pp. 187–206.
Efron, M. 2004. Cultural orientation: Classifying subjective documents by co-citation analysis. In Style and Meaning in Language, Art, Music, and Design: Papers from the 2004 AAAI Fall Symposium, Arlington, VA. Technical Report FS-04-07, pp. 41–48.
Hall, M., E. Frank, G. Holmes, B. Pfahringer, P. Reutemann and I.H. Witten. 2009. The weka data mining software: An update. ACM SIGKDD Explorations Newsletter 11(1), pp. 10–18.
Hargittai, E., J. Gallo and M. Kane. 2008. Cross-ideological discussions among conservative and liberal bloggers. Public Choice 134(1), pp. 67–86.
Hsueh, P.Y., P. Melville and V. Sindhwani. 2009. Data quality from crowdsourcing: a study of annotation selection criteria. In Proceedings of the NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing, pp. 27–35.
Kim, S.-M. and E. Hovy. 2007. Crystal: Analyzing predictive opinions on the web. In Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL, Prague), pp. 1056–1064.
Kittur, A., E.H. Chi and B. Suh. 2008. Crowdsourcing user studies with Mechanical Turk. In Proceeding of the 26th annual SIGCHI conference on Human factors in computing systems, Florence, pp. 453–456.
Kwon, N., S.W. Shulman and E. Hovy. 2006. Multidimensional text analysis for erule-making. In Proceedings of the 7th Annual International Conference on Digital Government Research (DG.O 2006, San Diego, CA), pp. 157–166.
Lerman, K., A. Gilder, M. Dredze and F. Pereira. 2008. Reading the markets: Forecasting public opinion of political candidates by news analysis. In Proceedings of the 22nd International Conference on Computational Linguistics (COLING 2008, Manchester), pp. 473–480.
Leskovec, J., M. McGlohon, C. Faloutsos, N. Glance and M. Hurst. 2007. Cascading behavior in large blog graphs: Patterns and a model. Society of Industrial and Applied Mathematics: Data Mining (SDM07, Minneanapolis, MN). Tech report (12 pp.): CMU-ML-06-113.
Lin, W.H., T. Wilson, J. Wiebe and A. Hauptmann. 2006. Which side are you on? identifying perspectives at the document and sentence levels. In Proceedings of the 10th Conference on Computational Natural Language Learning (CoNLL-X New York, NY), pp. 109–116.
Malouf, R. and T. Mullen. 2008. Taking sides: User classification for informal online political discourse. Internet Research 18(2), pp. 177–190.
Manning, C.D., P. Raghavan and H. Schütze. 2008. An Introduction to Information Retrieval. Cambridge: CUP.
McPherson, M., L. Smith-Lovin and J.M. Cook. 2001. Birds of a feather: Homophily in social net- works. Annual Review of Sociology, 27(1), pp. 415–444.
Moilanen, K. and S. Pulman. Multi-entity sentiment scoring. 2009 In Proceedings of the Recent Advances in Natural Language Processing (RANLP 2009, Borovets, Bulgaria), pp. 258–263.
Mullen, T. and R. Malouf. 2006. Preliminary investigation into sentiment analysis of informal political discourse. In Computational Approaches to Analyzing Weblogs: Papers from 2006 AAAI Spring Symposium (Stanford, CA), pp. 159–162.
Pang, B. and L. Lee. 2008. Opinion Mining and Sentiment Analysis, volume 2 of Foundations and Trends in Information Retrieval. Now Publishers.
Porter, M. 1980 An algorithm for suffix stripping. Program 14(1), p 3.
Snow, R., B. O’Connor, D. Jurafsky and A.Y. Ng. 2008. Cheap and fast—but is it good?: evaluating non-expert annotations for natural language tasks. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2008, Honolulu, HI), pp. 254–263.
Thomas, M., B. Pang and L. Lee. 2006. Get out the vote: Determining support or opposition from congressional floor-debate transcripts. In Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP 2006, Sydney, Australia), pp. 327–335.
Tremayne, M., N. Zheng, J.K. Lee and J. Jeong. 2006. Issue publics on the web: Applying network theory to the war blogosphere. Journal of Computer-Mediated Communication 12(1), pp. 290– 310.
Van Atteveldt, W., J. Kleinnijenhuis and N. Ruigrok. 2008a. Parsing, semantic networks, and political authority using syntactic analysis to extract semantic relations from Dutch newspaper articles. Political Science 16(4), pp. 428–446.
Van Atteveldt, W., J. Kleinnijenhuis, N. Ruigrok and S. Schlobach. 2008b. Good news or bad news? Conducting sentiment analysis on Dutch text to distinguish between positive and negative relations. Journal of Information Technology & Politics 5(1), pp. 73–94.
Yu, B., S. Kaufmann and D. Diermeier. 2008. Exploring the characteristics of opinion expressions for political opinion classification. In Proceedings of the 9th Annual International Conference on Digital Government Research, Partnerships for Public Innovation, Vol. 289 (DG.O 2008, Montreal), pp. 82–89.
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