Resumen
En los últimos años el análisis de sentimientos (minería de opinión) se ha venido desarrollando como una tendencia de investigación en los países desarrollados, en consecuencia, de los altos volúmenes de contenido que generan los usuarios de las redes sociales, a pesar de ser utilizada en varios sectores como el político, económico y de servicios ha sido escaso la aplicación de estas para le mejora de la comunicación en redes sociales. La presente investigación busca caracterizar la disposición que tienen los seguidores de un club deportivo antes, durante y después de un encuentro futbolístico en el contexto local con objetivo de servir de soporte en la mejora de las comunicaciones emitidas por el club deportivo en Facebook y Twitter.
Referencias
Alashri, S., Kandala, S. S., Bajaj, V., Ravi, R., Smith, K. L., & Deusouza, K. C. (2016). An Analysis of Sentiments on Facebook during An Analysis of Sentiments on Facebook during the, (August). https://doi.org/10.1109/ASONAM.2016.7752329
Bagić Babac, M., & Podobnik, V. (2016). A sentiment analysis of who participates, how and why, at social media sport websites. Online Information Review, 40(6), 814–833. https://doi.org/10.1108/OIR-02-2016-0050
Balazs, J. A., & Velásquez, J. D. (2016). Opinion Mining and Information Fusion: A survey. Information Fusion, 27, 95–110. https://doi.org/10.1016/j.inffus.2015.06.002
Barreto Idaly, López Wilson, S. L. (2013). Enmarcamiento cognitivo de la cultura política. Un análisis desde las redes sociales en Internet (Twitter).
Batrinca, B., & Treleaven, P. C. (2014). Social media analytics: a survey of techniques, tools and platforms. AI and Society, 30(1), 89–116.
https://doi.org/10.1007/s00146-014-0549-4
Bhat, S. Y., & Abulaish, M. (2013). Analysis and mining of online social networks: Emerging trends and challenges. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(6), 408–444. https://doi.org/10.1002/widm.1105
Chamlertwat, W., & Bhattarakosol, P. (2012). Discovering Consumer Insight from Twitter via Sentiment Analysis. J. Ucs, 18(8), 973–992. https://doi.org/10.1016/j.pragma.2013.12.003
Clavel, C., & Callejas, Z. (2016). Sentiment Analysis: From Opinion Mining to Human-Agent Interaction. IEEE Transactions on Affective Computing, 7(1), 74–93. https://doi.org/10.1109/TAFFC.2015.2444846
Delia, E. B., & Armstrong, C. G. (2015). Sponsoring the FrenchOpen: An Examination of Social Media Buzz and Sentiment. Journal of Sport Management, 29(2), 184–199. https://doi.org/10.1123/JSM.2013-0257
Devika, M. D., Sunitha, C., & Ganesh, A. (2016). Sentiment Analysis: A Comparative Study on Different Approaches. Procedia Computer Science, 87(June), 44–49. https://doi.org/10.1016/j.procs.2016.05.124
Diógenes, C. (2001). Herramientas informáticas para el análisis cualitativo.
Durahim, A. O., & Coskun, M. (2015). #iamhappybecause: Gross National Happiness through Twitter analysis and big data. Technological Forecasting and Social Change, 99, 92–105. https://doi.org/10.1016/j.techfore.2015.06.035
Funk, A., Li, Y., Saggion, H., Bontcheva, K., & Leibold, C. (2008). Opinion analysis for business intelligence applications. Proceedings of the First International Workshop on Ontology-Supported Business Intelligence, 308, 1–9. https://doi.org/10.1145/1452567.1452570
González, N., & Francisco, J. (2006). Estrategias De Marketing En Clubes Deportivos.
Grupo del Banco Mundial. (2015). Usuarios de Internet (por cada 100 personas). Retrieved from http://datos.bancomundial.org/indicador/IT.NET.USER.P2/countries?display=default
Hussein, D. M. E.-D. M. (2016). A survey on sentiment analysis challenges. Journal of King Saud University - Engineering Sciences, (April). https://doi.org/10.1016/j.jksues.2016.04.002
Jarm, K. (2014). Movie sentiment analysis based on public tweets. Elektrotehniˇski Vestnik, 81(4), 160–166. Retrieved from http://ev.fe.uni-lj.si/4-2014/Blatnik.pdf
Latam Digital Marketing. (2015). Redes Sociales Colombia. Retrieved from https://www.latamclick.com/estadisticas-de-facebook-y-twitter-en-colombia-2015/
Lima, A. C. E. S., De Castro, L. N., & Corchado, J. M. (2015). A polarity analysis framework for Twitter messages. Applied Mathematics and Computation, 270, 756–767. https://doi.org/10.1016/j.amc.2015.08.059
Ljajić, A., Ljajić, E., Spalević, P., Arsić, B., & Vučković, D. (2015). Sentiment analysis of textual comments in field of sport Sentiment analysis of textual comments in field of sport, (November).
María del Pilar Salas-Zárate, Estanislao López-López, Rafael Valencia-García, Nathalie Aussenac-Gilles, Ángela Almela, G. A.-H. (2014). A study on LIWC categories for opinion mining in Spanish reviews. Journal of Information Science, 40(14), 749–760.
Michael Loki Jomo. (2016). Limitations of Sentiment Analysis on Facebook Data -. International Journal of Social Sciences and Information Technologyocial, II(June 2016). Retrieved from http://www.ihub.co.ke/blogs/26786
Mostafa, M. M. (2013). More than words: Social networks’ text mining for consumer brand sentiments. Expert Systems with Applications, 40(10), 4241–4251. https://doi.org/10.1016/j.eswa.2013.01.019
Ouyang, Y., Guo, B., Zhang, J., Yu, Z., & Zhou, X. (2016). SentiStory: multi-grained sentiment analysis and event summarization with crowdsourced social media data. Personal and Ubiquitous Computing, (November). https://doi.org/10.1007/s00779-016-0977-x
Ravi, K., & Ravi, V. (2015). A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowledge-Based Systems, 89, 14–46. https://doi.org/10.1016/j.knosys.2015.06.015
Robaldo, L., & Di Caro, L. (2013). OpinionMining-ML. Computer Standards & Interfaces, 35(5), 454–469. https://doi.org/10.1016/j.csi.2012.10.004
Romero S., Y. A. (2010). Comparación de la dimensión estratégica del marketing en el deporte profesional venezolano.Caso: Fútbol (2003-2004) y Baloncesto (2008)*.
Salehan, M., & Kim, D. J. (2015). Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems, 81, 30–40. https://doi.org/10.1016/j.dss.2015.10.006
Salgado, A. (2007). Evaluación Del Rigor Metodológico Y Retos. Liberabit, 13(1729–4827), 71–78.
Sansom, K., & Jaroenwanit, P. (2016). A mediating role and influence of the relationship marketing success toward cluster productivity in Thailand. International Business Management, 10(4), 416–422. https://doi.org/10.3923/ibm.2016.416.422
Silver, L. (2013). The essentials of marketing research [electronic resource], (April 2015). Retrieved from http://encore.lib.warwick.ac.uk/iii/encore/record/C__Rb2603560__Sessentials of research methods __P0,4__Orightresult__U__X1?lang=eng&suite=cobalt
Tulankar, S., Athale, R., & Bhujbal, S. (2013). Sentiment Analysis of Equities using Data Mining Techniques and Visualizing the Trends. International Journal of Computer Science Issues, 10(4), 265–269.
Vilares, D., & Alonso, M. a. (2013). Una aproximaci on supervisada para la mineria de opiniones sobre tuits en espanol en base a conocimiento linguistico, 127–134.
Weichselbraun, A., Gindl, S., & Scharl, A. (2014). Enriching semantic knowledge bases for opinion mining in big data applications. Knowledge-Based Systems, 69(1), 78–85. https://doi.org/10.1016/j.knosys.2014.04.039
Woo, J., & Chen, H. (2016). Epidemic model for information diffusion in web forums: experiments in marketing exchange and political dialog. SpringerPlus, 5(1), 66. https://doi.org/10.1186/s40064-016-1675-x
Yu, Y., & Wang, X. (2015a). World Cup 2014 in the Twitter World: A big data analysis of sentiments in U.S. sports fans’ tweets. Computers in Human Behavior, 48(July), 392–400. https://doi.org/10.1016/j.chb.2015.01.075
Yu, Y., & Wang, X. (2015b). World Cup 2014 in the Twitter World: A big data analysis of sentiments in U.S. sports fans’ tweets. Computers in Human Behavior, 48, 392–400. https://doi.org/10.1016/j.chb.2015.01.075
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