Palabras clave
Análisis de Sentimientos
Minería de Opinión
Sentimental Data
Cómo citar
Resumen
El propósito del siguiente trabajo es exponer los beneficios y contras de la aplicación del análisis de sentimientos, en la búsqueda de Insights para el desarrollo de aplicaciones de consumo colaborativo, en su primera etapa se encuentra presentada su justificación y la definición de conceptos construida desde una revisión sistemática de lectura.
Referencias
AMA. (2004). Definition of Marketing. Retrieved from https://www.ama.org/AboutAMA/Pages/Definition-of-Marketing.aspx
Balazs, J. A., & Vel??squez, J. D. (2016). Opinion Mining and Information Fusion: A survey. Information Fusion, 27, 95–110. http://doi.org/10.1016/j.inffus.2015.06.002
Batrinca, B., & Treleaven, P. C. (2014). Social media analytics: a survey of techniques, tools and platforms. AI and Society, 30(1), 89–116. http://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. http://doi.org/10.1002/widm.1105
Bucklin, R. E., & Sismeiro, C. (2009). Click Here for Interact Insight: Advances in Clickstream Data Analysis in Marketing. Journal of Interactive Marketing (Mergent, Inc.), 23(1), 35–48. http://doi.org/10.1016/j.intmar.2008.10.004
Chamlertwat, W., & Bhattarakosol, P. (2012). Discovering Consumer Insight from Twitter via Sentiment Analysis. J. Ucs, 18(8), 973–992. http://doi.org/10.1016/j.pragma.2013.12.003
Durahim, A. O., & Coskun, M. (2015). #iamhappybecause: Gross National Happiness through Twitter analysis and big data. Technological Forecasting and Social Change, 99, 92–105. http://doi.org/10.1016/j.techfore.2015.06.035
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
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. http://doi.org/10.1016/j.amc.2015.08.059
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.
Mittal, a, & Goel, a. (2012). Stock Prediction Using Twitter Sentiment Analysis. Tomx.Inf.Elte.Hu, (June). Retrieved from http://tomx.inf.elte.hu/twiki/pub/Tudas_Labor/2012Summer/GoelMittal-StockMarketPredictionUsingTwitterSentimentAnalysis.pdf
Mostafa, M. M. (2013). More than words: Social networks’ text mining for consumer brand sentiments. Expert Systems with Applications, 40(10), 4241–4251. http://doi.org/10.1016/j.eswa.2013.01.019
Plattner, H. (2009). Mini guía: una introducción al Design Thinking + bootcamp bootleg, 28.
Ravi, K., & Ravi, V. (2015). A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowledge-Based Systems, 89, 14–46. http://doi.org/10.1016/j.knosys.2015.06.015
Rill, S., Reinel, D., Scheidt, J., & Zicari, R. V. (2014). PoliTwi: Early detection of emerging political topics on twitter and the impact on concept-level sentiment analysis. Knowledge-Based Systems, 69(1), 24–33. http://doi.org/10.1016/j.knosys.2014.05.008
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. http://doi.org/10.1016/j.dss.2015.10.006
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. http://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
Tobergte, D. R., & Curtis, S. (2013). Segmentation and Customer Insight in Contemporary ServicesMarketing Practice: Why Grouping Customers Is No Longer Enough. Journal of Chemical Information and Modeling, 53(9), 1689–1699. http://doi.org/10.1017/CBO9781107415324.004
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.
Woo, J., & Chen, H. (2016). Epidemic model for information diffusion in web forums: experiments in marketing exchange and political dialog. SpringerPlus, 5(1), 66. http://doi.org/10.1186/s40064-016-1675-x
Yee Liau, B., & Pei Tan, P. (2014). Gaining customer knowledge in low cost airlines through text mining. Industrial Management & Data Systems, 114(9), 1344–1359. http://doi.org/10.1108/IMDS-07-2014-0225
Yu, Y., & Wang, X. (2015). 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. http://doi.org/10.1016/j.chb.2015.01.075
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Creado a partir de la obra en http://www.poligran.edu.co/wpmgem