Influence of social networks on the analysis of sentiment applied to the political situation in Ecuador
DOI:
https://doi.org/10.29019/enfoqueute.v9n1.235Keywords:
Sentimen analysis, Twitter, Text mining, Stanford NLPAbstract
Knowing the opinion of a sector of the population can be as important to launch a product in marketing, as to know the opinion of voters, in politics. In Ecuador, the social network Twitter has become one of the main means of direct interaction between political figures and the population. So a study that reflects feelings in Spanish by idioms of each region, gives us a great opportunity to study the relationship between the level of acceptance on Twitter of a candidate and the election results. The contribution of this article is the analysis of feelings (SA) using a tool for NLP adapted to the variation of Spanish used in Ecuador, taking advantage of the fact that most of the literature has focused on the English language, while adaptations for languages, like Spanish, they are minimal and are still in process due to the complexity inherent in the language.
Downloads
References
Artigas, D., Muñoz, A., Luengo, F., Chourio, X., & Fernández, A. (abr de 2012). Caracterizando las elecciones venezolanas a través de Twitter. Caso: #26s. Anuario electrónico de estudios en Comunicación Social "Disertaciones, 5(1), 57-76.
Babiera, T. (3 de 2016). Técnicas para el análisis del sentimiento en Twitter: Aprendizaje Automático Supervisado y SentiStrength. Revista DÍGITOS, 33-50.
Baldasarri, S. (2017). Computación Afectiva: tecnología y emociones para mejorar la experiencia de usuario. 14,15.
Barberá, P., & Rivero, G. (2012). ¿Un tweet, un voto? Desigualdad en la discusión política en Twitter. I Congreso Internacional en Comunicación Política y Estrategias de Campaña.
Barbosa, L., & Feng, J. (2010). Robust sentiment detection on twitter from biased and noisy data. En Proceedings of the 23rd International Conference on Computational Linguistics, 36–44.
Bernard, J., Zhang, M., Sobel, K., & Chowdury, A. (2009). Twitter power: Tweets as electronic word of mouth. Journal of the American society for information science and technology, 60(11), 2169– 2188.
Bo, P., Lee, L., & Vaithyanathan, S. (2002). Thumbs up? Sentiment classification using machine learning techniques. PROCEEDINGS OF EMNLP, 79–86.
Bravo-Marquez, F., Mendoza, M., & Poblete, B. (2014). Meta-level sentiment models for big social data analysis. Knowledge-Based Systems, 69(1), 86–99.
Bustos, J., & Capilla, L. (2013). Twitter y la polarización del debate político: análisis del caso #objetivodeguindos y #aznara3. Revista Historia y Comunicación Social, 18, 499-509.
Caton, S., Hall, M., & Weinhardt, C. (2015). How do politicians use Facebook? An applied Social Observatory. Big Data & Society, 2(2).
Congosto, M. (2015). Elecciones Europeas 2014: Viralidad de los mensajes en TwitterRedes. Revista Hispana para el Análisis de Redes Sociales.
Da Silva, N., Hruschka, E., & Hruschka, E. (2014). Tweet sentiment analysis with classifier ensembles. Decision Support Systems, 66, 170–179.
Dang-Xuan, L., Stieglitz, S., Wladarsch, J., & Neuberger, C. (2013). An Investigation of Influentials and the Role of Sentiment in Political Communication on Twitter During Election Periods. Information, Communication & Society, 16(5), 795–825.
Group, S. N. (18 de 02 de 2018). Stanford NLP Group. Obtenido de Stanford NLP Group: https://nlp.stanford.edu/software/
Guevara, M., Pino, D., Mendoza, M., Pacheco, C., & Olivares, M. (August de 2013). Chile y el Ecosistema de las Elecciones Polıticas en Twitter. IV Congreso Internacional de Informática del Norte de Chile, Coquimbo-Chile.
Guo, L., & Vargo, C. (2015). The Power of Message Networks: A Big-Data Analysis of the Network Agenda Setting Model and Issue Ownership. Mass Communication and Society, 18(5), 557–576.
Jungherr, A. (2015). Analyzing Political Communication with Digital Trace Data: The Role of Twitter Messages in Social Science Research. Cham: Springer International Publishing Switzerland.
Lipka, N. (18 de 02 de 2018). Modeling Non-Standard Text Classification Tasks. Bauhaus-Universität Weimar , Germany.
Manning, C., Surdeanu, M., Bauer, J., Finkel, J., Bethard, S., & McClosky, D. (2014). The Stanford CoreNLP Natural Language Processing Toolkit. Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 55-60.
Martínez-Cámara, E., Martín-Valdivia, M., Ureña-López, L., & Montejo-Ráez, A. (2012). Sentiment analysis in twitter. Natural Language Engineering, 1-28.
Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093-1113.
O´Connor, B., Krieger, M., & Ahn, D. (May 23-26 de 2010). Tweetmotif: Exploratory search and topic summarization for twitter. William W. Cohen y Samuel Gosling, editores, Proceedings of the Fourth International Conference on Weblogs and Social Media.
Padró, L., & Stanilovsky, E. (May de 2012). Freeling 3.0: Towards wider multilinguality. Proceedings of the Language Resources and Evaluation Conference (LREC), Istanbul,
Turkey.
Prata, D., Soares, K., Silva, M., Trevisan, D., & Letouze, P. (2016). Social Data Analysis of Brazilian’s Mood from Twitter. International Journal of Social Science and Humanity, 6(3), 179–183.
Robins, D., Frati, F., Alvarez, J., Texier, J., & Loto, L. (2012). Balotaje Argentina 2015 a partir de un análisis de sentimiento de tweets. Zenodo.
Turney, P. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. ACL, 417–424.
Vargo, C., Guo, L., McCombs, M., & Shaw, D. (2014). Network Issue Agendas on Twitter During the 2012 U.S. Presidential Election. Journal of Communication, 64, 296–316.
Vinodhini, G., & Chandrasekaran, R. (2012). Sentiment analysis and opinion mining: A survey. International Journal, 2(6).
Wilson, T., Kozareva, Z., Nakov, P., Rosenthal, S., Stoyanov, V., & Ritter, A. (2013). Sentiment analysis in twitter. Proceedings of the International Workshop on Semantic Evaluation. SemEval.
Yu, Y., & Wang, X. (2015). World Cup 2014 in the Twitter World: A big data analysis of sentiments in U.S. sports fans’ tweets. En Computers in Human Behavior, 48, 392–400.
Published
How to Cite
Issue
Section
License
The authors retain all copyrights ©.
- The authors retain their trademark and patent rights, as well as rights to any process or procedure described in the article.
- The authors retain the right to share, copy, distribute, perform, and publicly communicate the article published in Enfoque UTE (for example, post it in an institutional repository or publish it in a book), provided that acknowledgment of its initial publication in Enfoque UTE is given.
- The authors retain the right to publish their work at a later date, to use the article or any part of it (for example, a compilation of their work, lecture notes, a thesis, or for a book), provided that they indicate the source of publication (authors of the work, journal, volume, issue, and date).