Abstract
Readily observable communications found on Internet social media sites can play a prominent role in spreading information which, when accompanied by subjective statements, can indicate public sentiment and perception. A key component to understanding public opinion is extraction of the aspect toward which sentiment is directed. As a result of message size limitations, Twitter users often share their opinion on events described in linked news stories that they find interesting. Therefore, a natural language analysis of the linked news stories may provide useful information that connects the Twitter-expressed sentiment to its aspect. Our goal is to monitor sentiment towards political actors by evaluating Twitter messages with linked event code data. We introduce a novel link-following approach to automate this process and correlate sentiment-bearing Twitter messages with aspect found in connected news articles. We compare multiple topic extraction approaches based on the information provided in the event codes, including the Goldstein scale, a simple decision tree model, and spin-glass graph clustering. We find that while Goldstein scale is uncorrelated with public sentiment, graph-based event coding schemes can effectively provide useful and nuanced information about the primary topics in a Twitter dataset.
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References
Ringsquandl, M., Petkovic, D.: Analyzing political sentiment on Twitter. In: AAAI Spring Symposium (2013)
Schrodt, P., Beieler, J., Idris, M.: Three’s a Charm? Open event data coding with EL: DIABLO, PETRARCH, and the open event data alliance. In: International Studies Association Meeting (2014)
Schrodt, P.A.: CAMEO: conflict and mediation event observations event and actor codebook. Pennsylvania State University, March 2012
Goldstein, J.S.: A conflict-cooperation scale for WEIS events data. J. Confl. Resolut. 36(2), 369–385 (1992)
Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1, 81–106 (1986)
Reichardt, J., Bornholdt, S.: Statistical mechanics of community detection. Phys. Rev. E 74, 016110 (2006)
Musto, C., Semararo, G., Polignano, M.: A comparison of lexicon-based approaches for sentiment analysis of microblog posts. In: 8th International Workshop on Information Filtering and Retrieval (2014)
Shuyo, N.: Short text language detection with infinity-gram. NAIST Seminar (2012)
Hothorn, T., Hornik, K., Zeileis, A.: Unbiased recursive partitioning: a conditional inference framework. J. Comput. Graph. Stat. 15(3), 651–674 (2006)
Wu, F.-Y.: The Potts model. Rev. Mod. Phys. 54, 235 (1982)
Newman, M.E.J., Girvan, M.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99, 7821–7826 (2002)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(4–5), 993–1022 (2003)
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Overbey, L.A., Batson, S.C., Lyle, J., Williams, C., Regal, R., Williams, L. (2017). Linking Twitter Sentiment and Event Data to Monitor Public Opinion of Geopolitical Developments and Trends. In: Lee, D., Lin, YR., Osgood, N., Thomson, R. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2017. Lecture Notes in Computer Science(), vol 10354. Springer, Cham. https://doi.org/10.1007/978-3-319-60240-0_26
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DOI: https://doi.org/10.1007/978-3-319-60240-0_26
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