Election analysis in Colombia and Venezuela 2015 through sentiment analysis and Twitter
DOI:
https://doi.org/10.18046/syt.v14i39.2349Keywords:
Sentiment analysis, elections, natural language, Twitter, Apicultor, spammer.Abstract
This paper presents an analysis of the accounts of the main candidates in the regional elections on October 25, 2015 in Colombia (Bogotá, Medellin and Cali) and the official hashtags of the two main parties for parliamentary elections on December 6, 2015 in Venezuela (PSUV and MUD) in order to determine the positive or negative trends and compare them with the results of the respective elections. To develop the analysis, we resorted to the technique of sentiment analysis own of data mining and the use of descriptive statistics, concluding that sentiment analysis for estimating trends requires processes to monitor retweets, if you want acceptable results.
References
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Bifet, A., & Frank, E. (2010). Sentiment knowledge discovery in Twitter streaming data. Lecture Notes in Computer Science, 6332 LNAI, 1–15. doi :10.1007/978-3-642-16184-1_1
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Cerón-Guzmán, J. A., & León, E. (2015). Detecting social spammers in Colombia 2014 presidential election: Lecture Notes in Computer Science, 9414 - Advances in artificial intelligence and its applications, (pp. 121-141). Switzerland: Springer.
Choy, M., Cheong, M. L. F., Laik, M. N., & Shung, K. P. (2011). A sentiment analysis of Singapore Presidential Election 2011 using Twitter data with census correction. Retrieved from: https://arxiv.org/abs/1108.5520
Elecciones parlamentarias de Venezuela de 2015. Retrieved from: https://es.wikipedia.org/wiki/Elecciones_parlamentarias_de_Venezuela_de_2015
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Mamprin, A. (2015, Oct. 21). ¿Cómo les va a los candidatos a la Alcaldía de Cali en Twitter? El País. Cali. Retrieved from: http://www.elpais.com.co/elpais/elecciones-2015/noticias/como-les-va-candidatos-alcaldia-cali-twitter
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Stirland, S. L. (2008). Obama’s secret weapons: Internet, databases and psychology. Retrieved from: https://www.wired.com/2008/10/obamas-secret-w/
Thelwall, M., Buckley, K., & Paltoglou, G. (2011). Sentiment in Twitter events. Journal of the American Society for Information Science and Technology, 62(2), 406-418. doi:10.1002/asi.21462
Tumasjan, A., Sprenger, T. O., Sandner, P. G., & Welpe, I. M. (2011). Election forecasts with Twitter: How 140 characters reflect the political landscape. Social Science Computer Review, 29(4), 402-418. doi:10.1177/0894439310386557
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. doi:10.1074/jbc.M501708200
Turney, P. D. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th annual meeting on Association for Computational Linguistics (pp. 417-424). Stroudsburg PA:ACL.
Wang, H., Can, D., Kazemzadeh, A., Bar, F., & Narayanan, S. (2012). A system for real-time Twitter sentiment analysis of 2012 U.S. presidential election cycle. Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, (pp. 115-120). Stroudsburg, PA: ACL
Wolfram, M. S. A. (2010). Modelling the stock market using Twitter. Scotland, UK: University of Edinburgh.
Agarwal, A., Xie, B., Vovsha, I., Rambow, O., & Passonneau, R. (2011, June). Sentiment analysis of twitter data. In Proceedings of the Workshop on Languages in Social Media (pp. 30-38). Stroudsburg PA: ACL.
Anjaria, M., & Guddeti, R. M. R. (2014). Influence factor based opinion
mining of Twitter data using supervised learning. In Communication Systems and Networks (COMSNETS), 2014 Sixth International Conference on (pp. 1–8). http://doi.org/10.1109/COMSNETS.2014.6734907
APIs Sentiment analysis (2012). Retrieved May 20, 2012, from https://store.apicultur.com/
Bermingham, A., & Smeaton, A. F. (2011). On using Twitter to monitor political sentiment and predict election results, In: Sentiment Analysis where AI meets Psychology (SAAIP) Workshop at the International Joint Conference for Natural Language Processing (IJCNLP), 13th November 2011, Chiang Mai, Thailand.
Bifet, A., & Frank, E. (2010). Sentiment knowledge discovery in Twitter streaming data. Lecture Notes in Computer Science, 6332 LNAI, 1–15. doi :10.1007/978-3-642-16184-1_1
Brown, E. (2012). Will twitter make you a better investor? A look at sentiment, user reputations and their effect on the stock market. In Proceedings of Southern Association for Information Systems (SAIS) (pp. 36.42).
Cerón-Guzmán, J. A., & León, E. (2015). Detecting social spammers in Colombia 2014 presidential election: Lecture Notes in Computer Science, 9414 - Advances in artificial intelligence and its applications, (pp. 121-141). Switzerland: Springer.
Choy, M., Cheong, M. L. F., Laik, M. N., & Shung, K. P. (2011). A sentiment analysis of Singapore Presidential Election 2011 using Twitter data with census correction. Retrieved from: https://arxiv.org/abs/1108.5520
Elecciones parlamentarias de Venezuela de 2015. Retrieved from: https://es.wikipedia.org/wiki/Elecciones_parlamentarias_de_Venezuela_de_2015
Jiang, L., Yu, M., Zhou, M., Liu, X., & Zhao, T. (2011, June). Target-dependent twitter sentiment classification. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1 (pp. 151-160). Stroudsburg PA:ACL.
Kiplinger. (2011). Market’s mood in a tweet. Kiplingers Personal Finance, (4), 14.
Liu, B. & Zhang, L. (2012). A survey of opinion mining and sentiment analysis. In Mining Text Data (pp. 415-463). US: Springer.
Liu, B. (2010). Sentiment analysis and subjectivity. In Handbook of natural language processing (2a ed.), (pp. 627-666). Boca Raton, FL: Chapman and Hall/CRC.
Mamprin, A. (2015, Oct. 21). ¿Cómo les va a los candidatos a la Alcaldía de Cali en Twitter? El País. Cali. Retrieved from: http://www.elpais.com.co/elpais/elecciones-2015/noticias/como-les-va-candidatos-alcaldia-cali-twitter
Mao, H., Counts, S., & Bollen, J. (2011). Predicting Financial Markets: Comparing Survey, News, Twitter and Search Engine Data [arXiv Preprint, 10]. Retrieved from: https://arxiv.org/abs/1112.1051
Nguyen, V. D., Varghese, B., & Barker, A. (2013). The royal birth of 2013: Analyzing and visualizing public sentiment in the UK using Twitter. In Big Data, 2013 IEEE International Conference on (pp. 46-54). doi:10.1109/BigData.2013.6691669
Pang, B., Lee, L., & Vaithyanathan, S. (2002, July). Thumbs up? Sentiment classification using machine learning techniques. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10 (pp. 79-86). Stroudsburg PA: ACL.
Pasek, J. (2006). America’s youth and community engagement: How use of mass media is related to civic activity and political awareness in 14- to 22-year-olds. Communication Research, 33(3), 115-135. doi:10.1177/0093650206287073
Pla, F., & Hurtado, L. F. (2013). ELiRF-UPV en TASS-2013: análisis de sentimientos en Twitter. In XXIX Congreso de la Sociedad Española para el Procesamiento del Lenguaje Natural (SEPLN 2013). TASS (pp. 220-227).
Porshnev, A., Redkin, I., & Shevchenko, A. (2013). Machine learning in prediction of stock market indicators based on historical data and data from Twitter sentiment analysis. In 2013 IEEE 13th International Conference on Data Mining Workshops (pp. 440–444): IEEE. doi:10.1109/ICDMW.2013.111
Qureshi, P. A. R., Memon, N., Wiil, U. K., & Karampelas, P. (2011). Detecting social polarization and radicalization. International Journal of Machine Learning and Computing, 1(1), 49-57.
Razzaq, M. A., Qamar, A. M., & Bilal, H. S. M. (2014). Prediction and analysis of Pakistan election 2013 based on sentiment analysis. In Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on (pp. 700–703). doi:10.1109/ASONAM.2014.6921662
REST APIs (s.f) [Tweeter Developer Documentation blog]. Retrieved from: https://dev.twitter.com/rest/public
Selvan, L. G. S. & Moh, T. S. (2015). A framework for fast-feedback opinion mining on Twitter data streams. In Collaboration Technologies and Systems (CTS), 2015 International Conference on (pp. 314-318). doi:10.1109/CTS.2015.7210440
Stirland, S. L. (2008). Obama’s secret weapons: Internet, databases and psychology. Retrieved from: https://www.wired.com/2008/10/obamas-secret-w/
Thelwall, M., Buckley, K., & Paltoglou, G. (2011). Sentiment in Twitter events. Journal of the American Society for Information Science and Technology, 62(2), 406-418. doi:10.1002/asi.21462
Tumasjan, A., Sprenger, T. O., Sandner, P. G., & Welpe, I. M. (2011). Election forecasts with Twitter: How 140 characters reflect the political landscape. Social Science Computer Review, 29(4), 402-418. doi:10.1177/0894439310386557
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. doi:10.1074/jbc.M501708200
Turney, P. D. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In Proceedings of the 40th annual meeting on Association for Computational Linguistics (pp. 417-424). Stroudsburg PA:ACL.
Wang, H., Can, D., Kazemzadeh, A., Bar, F., & Narayanan, S. (2012). A system for real-time Twitter sentiment analysis of 2012 U.S. presidential election cycle. Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, (pp. 115-120). Stroudsburg, PA: ACL
Wolfram, M. S. A. (2010). Modelling the stock market using Twitter. Scotland, UK: University of Edinburgh.
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2016-12-01
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