-
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).