Detección de patrones y grupos de sentimientos a partir del análisis de tuits políticos

Rocío Abascal-Mena, Erick López-Ornelas, Sergio Zepeda-Hernández

Resumen


Este artículo presenta un ejemplo de cómo pueden analizarse los tuits aplicando un léxico de opinión en dos distintos corpus conformados por tuits generados durante diferentes movimientos políticos y sociales en Francia. Ambos corpus corresponden a las mismas fechas con el propósito de llevar a cabo un análisis, durante el paso del tiempo, sobre las diferencias y similitudes entre los sentimientos. La investigación proporciona algunos elementos claves en los que se pueden apreciar períodos y grupos de sentimientos en una población en particular. El análisis efectuado a cada corpus es mostrado de manera gráfica de manera a poder visualizar patrones en el estado de ánimo de dos movimientos sociales diferentes. La metodología propuesta puede ser aplicada para la detección de importantes puntos de inflexión, incluso patrones de sentimientos, en mensajes enviados en manifestaciones sociales y políticas a través del
uso de las redes sociales.

Texto completo:

1672-1691 PDF

Referencias


M. Thelwall, K. Buckley, G. Paltoglou, “Sentiment in Twitter events”. Journal of the American Society for Information Science and Technology, 62(2), 2011, pp. 406-418.

J. Bollen, H. Mao, Modeling public mood and emotion: Twitter sentiment and socio-economic phenomena. Proceedings of the Fifth International AAAI Conference on Weblogs and Social Media. 2011.

B. O’Connor, R. Balasubramanyan, B. R. Routledge, N. A. Smith, From Tweets to Polls: Linking Text Sentiment to Public Opinion Time Series. In Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media. 2010.

A. Tumasjan, T. O. Sprenger, P. G. Sandner, I. M. Welpe, Predicting Elections with Twitter: What 140 Characters Reveal about Political Sentiment. Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media. 2010. pp. 178-185.

V. Wijaya, A. Erwin, M. Galinium, W. Muliady, Automatic mood classification of Indonesian tweets using linguistic approach. 2013 International Conference on Information Technology and Electrical Engineering (ICITEE). 2013. pp. 41-46.

V. Martínez, V. M. González, Sentiment Characterization of an Urban

Environment via Twitter. In Ubiquitous Computing and Ambient Intelligence.

Context-Awareness and Context-Driven Interaction. 2013. pp. 394-397. Springer International Publishing.

K. Z. Bertrand, M. Bialik, K. Virdee, A. Gros, Y. Bar-Yam, Sentiment in New York City: A High Resolution Spatial and Temporal View.arXiv preprint arXiv: 1308.5010.2013.

V. Lampos, T. Lansdall-Welfare, R. Araya, N. Cristianini, Analysing mood

patterns in the United Kingdom through twitter content. arXiv preprint

arXiv:1304.5507. 2013.

M. Cha, H. Haddadi, F. Benevenuto, K. P. Gummadi, Measuring User Influence in Twitter: The Million Follower Fallacy. Proceedings of the 4th International AAAI Conference on Weblogs and Social Media (ICWSM), Washington, DC, 2010.

J. E. Chung, E. Mustafaraj, Can collective sentiment expressed on Twitter predict political elections? W. Burgard & D. Roth (Eds.), Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence (AAAI 2011). 2011. pp. 1768-1769. Menlo Park, CA: AAAI Press.

P. S. Dodds, C. M. Danforth, “Measuring the happiness of large-scale written expression: Songs, blogs, and presidents”. Journal of Happiness Studies, 11(4). 2010. pp. 441-456.

A. Gruzd, S. Doiron, P. Mai, Is happiness contagious online? A case of Twitter and the 2010 Winter Olympics. Proceedings of the 44th Hawaii International Conference on System Sciences. Washington, DC: IEEE Computer Society. 2011.

A. D. I. Kramer, An unobtrusive behavioral model of "Gross National Happiness". Proceedings of CHI 2010. 2010. pp. 287-290. New York: ACM Press.

S. Baccianella, A. Esuli, F. Sebastiani, SentiWordNet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. Proceedings of the Seventh Conference on International Language Resources and Evaluation, 2010. pp. 2200-2204. European Language Resources Association.

C. Fellbaum, “WordNet: An Electronic Database. Cambridge”. MA: MIT Press. 1998.

T. Wilson, J. Wiebe, P. Hoffmann, Recognizing contextual polarity in phrase-level sentiment analysis. Proceedings of the Conference on Empirical Methods in Natural Language Processing. ACL. 2005.

P. J. Stone, C. D. Dexter, S. S. Marshall, D. Ogilvie, “The General Inquirer: A Computer Approach to Content Analysis”. Cambridge, MA: MIT Press. 1966.

L. Bing, “Sentiment Analysis and Opinion Mining”. Morgan & Claypool Publishers, 2012.

D. Maynard, K. Bontcheva, D. Rout, Challenges in developing opinion mining tools for social media. Proceedings of @ NLP can u tag#

user_generated_content, 2012.

B. Pang, L. Lee, Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 1(1–2), 2008. pp. 1–135.

R. González-Ibáñez, S. Muresan, N. Wacholder, Identifying sarcasm in Twitter: a closer look. Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies. Short papers-Volume 2. Association for Computational Linguistics, 2011. pp. 581-586.

The R Project for Statistical Computing. http://www.r-project.org/. Abril 2014.

R. Abascal-Mena, R. Lema, F. Sèdes, From Tweet to Graph: Social Network Analysis for Semantic Information Extraction. IEEE Eighth International Conference on Research Challenges in Information Science. Marrakesh, Morocco. ISBN: 978-1-4799-2393-9. 2014. pp. 227-236.


Enlaces refback

  • No hay ningún enlace refback.




URL de la licencia: https://creativecommons.org/licenses/by/3.0/deed.es

Licencia Creative Commons    Esta revista está bajo una Licencia Creative Commons Atribución 3.0 No portada.