Abstract
Due to its popularity, Twitter is currently one of the major players in the global network, which has established a new form of communication: the microblogging. Twitter has become an essential media network for the follow-up, diffusion and coordination of events of diverse nature and importance (Gonzalez-Agirre et al. in Multilingual central repository version 3.0. Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC’12). Istanbul, Turkey, 2012, [1]), such as a presidential campaign, a disaster situation, a war or the repercussion of information. In such scenario, it is considered a relevant source of information to know the opinions that are emitted about different issues or people. This research proposes the evaluation of several supervised classification algorithms to address the problem of opinion mining on Twitter.
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References
Gonzalez-Agirre A, Laparra E, Laparra G (2012) Multilingual central repository version 3.0. In: Proceedings of the eight international conference on language resources and evaluation (LREC’12), May 2012. European Language Resources Association (ELRA), Istanbul, Turkey
Rousseeuw P (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20(1):53–65 [Online]. Disponible: http://dx.doi.org/10.1016/0377-0427(87)90125–7
Wilcoxon F (1945) Individual comparisons by ranking methods. Biometrics Bull 1(6):80–83
Riloff E, Janyce W (2003) Learning extraction patterns for subjective expressions. In: Proceedings of the 2003 conference on empirical methods in natural language processing, EMNLP’03. Association for Computational LinguisticsStroudsburg, PA, USA, pp 105–112
Lis-Gutiérrez JP, Gaitán-Angulo M, Henao LC, Viloria A, Aguilera-Hernández D, Portillo-Medina R (2018) Measures of concentration and stability: two pedagogical tools for industrial organization courses. In: Tan Y, Shi Y, Tang Q (eds) Advances in swarm intelligence. ICSI 2018. Lecture notes in computer science, vol 10942. Springer, Cham
Zhao WX, Weng J, He J, Lim EP, Yan H (2011) Comparing twitter and traditional media using topic models. In: 33rd European conference on advances in information retrieval (ECIR11). Springer-Verlag, Berlin, Heidelberg, pp 338–349
Viloria A, Gaitan-Angulo M (2016) Statistical adjustment module advanced optimizer planner and SAP generated the case of a food production company. Indian J Sci Technol 9(47). https://doi.org/10.17485/ijst/2016/v9i47/107371
Villada F, Muñoz N, García E (2012) Aplicación de las Redes Neuronales al Pronóstico de Precios en Mercado de Valores, Información tecnológica 23(4):11–20
Sapankevych N, Sankar R (2009) Time series prediction using support vector machines: a survey. IEEE Comput Intell Mag 4(2):24–38
Viloria A, Lezama OBP (2019) Improvements for determining the number of clusters in k-means for innovation databases in SMEs. Procedia Comput Sci 151:1201–1206
Toro EM, Mejia DA, Salazar H (2004) Pronóstico de ventas usando redes neuronales. Scientia et technica 10(26):12–25
Hernández JA, Burlak G, Muñoz Arteaga J, Ochoa A (2006) Propuesta para la evaluación de objetos de aprendizaje desde una perspectiva integral usando minería de datos. En A. Hernández y J. Zechinelli (eds.) Avances en la ciencia de la computación. Universidad Autónoma de México, México, pp 382–387
Romero C, Ventura S (2007) Educational data mining: a survey from 1995 to 2005. Expert Syst Appl 33(1):135–146
Romero C, Ventura S (2010) Educational data mining: a review of the state of the art. Syst Man Cybern Part C Appl Rev IEEE Trans 40(6):601–618
Choudhury A, Jones J (2014) Crop yield prediction using time series models. J Econ Econ Educ Res 15:53–68
Scheffer T (2004) Finding association rules that trade support optimally against confidence. Intell Data Anal 9(4):381–395
Ruß G (2009) Data mining of agricultural yield data: a comparison of regression models. In: Perner P (eds) Advances in data mining. Applications and theoretical aspects, ICDM 2009. Lecture notes in computer science, vol 5633
Viloria A, Lis-Gutiérrez JP, Gaitán-Angulo M, Godoy ARM, Moreno GC, Kamatkar SJ (2018) Methodology for the design of a student pattern recognition tool to facilitate the teaching - learning process through knowledge data discovery (Big Data). In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture notes in computer science, vol 10943. Springer, Cham
Berrocal JLA, Figuerola CG, Rodrıguez AZ (2013) Reina at RepLab2013 topic detection task: community detection. In: Proceedings of the fourth international conference of the CLEF initiative
Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH (2009) The weka data mining software: an update. SIGKDD Explor Newsl 11(1):10–18
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Silva, J. et al. (2021). Classification, Identification, and Analysis of Events on Twitter Through Data Mining. In: Gunjan, V.K., Zurada, J.M. (eds) Proceedings of International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications. Advances in Intelligent Systems and Computing, vol 1245. Springer, Singapore. https://doi.org/10.1007/978-981-15-7234-0_89
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