Studia Medioznawcze: logo

Studia Medioznawcze Media Studies 3 (74) 2018

Okładka

Political sentiment analysis of press freedom

Krzysztof Rybiński
(Academy of Finance and Business Vistula, Warsaw/Akademia Finansów i Biznesu Vistula, Warszawa)

PDF English version of the article

This article applies computer political sentiment analysis to news stories mentioning government officials published by major news portals in Kazakhstan and Poland. Surprisingly, while Kazakhstan is classified in freedom rankings as “not free”, its major media publish more critical views about the government than media in Poland, a country classified as “free” or “mostly free”. The presented methodology also allows to derive the real political power structure. The article shows that international freedom rankings can be improved by political sentiment analysis to local news.

KEYWORDS

political sentiment analysis, press freedom ranking, Kazakhstan, Poland

BIBLIOGRAPHY

  • Alpaydin, E. ( 2016). “Machine Learning: The New AI”, MIT Press.
  • Bakken, P. F., Bratlie, T. A., Marco, C., & Gulla, J. A. (2016). Political News Sentiment Analysis for Underresourced Languages. In COLING (pp. 2989–2996).
  • Benoit, K. (2017). “quanteda: Quantitative Analysis of Textual Data”, 15 August 2017.
  • Bosco, C., Patti, V., Bolioli, A. (2013). “Developing Corpora for Sentiment Analysis: The Case of Irony and Senti-TUT, Knowledge-Based Approaches to Concept-Level Sentiment Analysis”, IEEE Intelligent Systems.
  • Carbonell, J. (1979). “Subjective Understanding: Computer Models of Belief Systems. PhD thesis, Yale.
  • Ceron, A., Curini, L., Iacus, S. M. (2015). “Using sentiment analysis to monitor electoral campaigns: Method matters evidence from the United States and Italy”. Social Science Computer Review, 33 (1), 3–20.
  • Ecker, A. (2017). Estimating policy positions using social network data: cross-validating position estimates of political parties and individual legislators in the Polish parliament. Social Science Computer Review, 35 (1), 53–67.
  • Fortuny, E. J., Smedt, T. D., Martens, D. & Daelemans, W. (2012). “ Media coverage in times of political crisis: A text mining approach”, Expert Systems with Applications 39 (2012) 11616–11622.
  • Franch, F. (2013). (Wisdom of the Crowds)2: 2010 UK election prediction with social media. Journal of Information Technology & Politics, 10, 57–71. doi:10.1080/19331681.2012.705080.
  • Gogołek, W., Jaruga, D., Kowalik, K. & Celiński, P. (2015). Z badań nad wykorzystaniem rafinacji informacji sieciowej Wybory prezydenckie i parlamentarne 2015. Studia Medioznawcze, 3 (62), 31–40. (In Polish).
  • González-Bailón, S., Morales, G. D. F., Mendoza, M., Khan, N. & Castillo, C. (2014). Cable news coverage and online news stories: A large-scale comparison of digital media content. In: Annual Meeting of the International Communication Association (ICA). Harris, Z. (1954). “Distributional Structure”. Word. 10 (2/3): 146–62.
  • Loukachevitch N., Levchik A., 2016. “Creating a General Russian Sentiment Lexicon”. In: Proceedings of Language Resources and Evaluation Conference LREC-2016.
  • McAfee, A., Brynjolfsson, E. (2017). “Machine, Platform, Crowd: Harnessing Our Digital Future”, W. W. Norton & Company.
  • Melville, P., Gryc, W., Lawrence, R. D. (2009). “Sentiment analysis of blogs by combining lexical knowldge with text classification”. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1275–1284). ACM.
  • Ngai, E. W. T., Lee, P. T. Y., (2016). A Review of the literature on Applications of Text Mining in Policy Making. In: PACIS (p. 343).
  • Niculae, V., Suen, C., Zhang, J., Danescu-Niculescu-Mizil, C., & Leskovec, J. ( 2015), May. Quotus: The structure of political media coverage as revealed by quoting patterns. In: Proceedings of the 24th International Conference on World Wide Web (pp. 798–808). International World Wide Web Conferences Steering Committee.
  • Ogrodniczuk, M., Kopeć, M. (2017). “Lexical Correction of Polish Twitter Political Data”. In: Proceedings of the Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (pp. 115–125).
  • Piryani, R., Madhavi, D. & Singh, V. K. (2017). “Analytical mapping of opinion mining and sentiment analysis research during 2000–2015”. Information Processing & Management, 53 (1), 122–150.
  • Ravi, K., & Ravi, V. (2015). “A survey on opinion mining and sentiment analysis: tasks, approaches and applications”. Knowledge-Based Systems, 89, 14–46.
  • Rill, S., Reinel, D., Scheidt, J., & Zicari, R.V. (2014). “Early detection of emerging political topics on Twitter and the impact on concept-level sentiment analysis”, Knowledge-Based Systems 69 (2014): 24–33.
  • Sindhwani, V., Melville, P. (2008). “Document-word co-regularization for semi-supervised sentiment analysis”, In: Eighth IEEE International Conference on Data Mining, 1025–1030, December 2008.
  • Sobkowicz, P., Sobkowicz, A. (2012). Two-year study of emotion and communication patterns in a highly polarized political discussion forum. Social Science Computer Review, 30 (4), 448–469.
  • Sobkowicz, P., Kaschesky, M. & Bouchard,G. (2012). Opinion mining in social media: Modeling, simulating, and forecasting political opinions in the web, Government Information Quarterly 29 (2012): 470–479.
  • Taddy, M. (2013). “Measuring Political Sentiment on Twitter: Factor Optimal Design for Multinomial Inverse Regression”, Technometrics.
  • Tumasjan, A., Sprenger, T. O., Sandner, P. G. & Welpe, I. M. (2010). “Predicting elections with twitter: What 140 characters reveal about political sentiment”. Icwsm, 10 (1), 178–185.
  • Wild, F. (2015). lsa: Latent Semantic Analysis, 8 May 2015, Wilks, Y., Bien, J. (1984). Beliefs, points of view and multiple environments, In Proceedings of the international NATO symposium on artificial and human intelligence, pp. 147–171, USA, New York, NY: Elsevier North-Holland, Inc.
  • Zaśko-Zielińska, M., Piasecki, M. & Szpakowicz, S. (2015) A LargeWordnet-based Sentiment Lexicon for Polish, Proceedings of the International Conference Recent Advances in Natural Language Processing (RANLP’2015), pp. 721–730.