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Abstract
Con el rápido desarrollo de la tecnología de la información, los clientes no sólo compran en línea, sino que también publican comentarios en las redes sociales. Este Contenido Generado por el Usuario (CGU) puede ser útil para comprender las experiencias de compra de los clientes e influir en las intenciones de compra de los futuros clientes. Por lo tanto, la inteligencia empresarial y la analítica son cada vez más defendidas como una manera de analizar el (CGU) en las redes sociales y apoyar las actividades de marketing de las empresas. Sin embargo, debido a su estructura abierta, el (CGU) como las revisiones de los clientes pueden ser difíciles de analizar, y las empresas lo encuentran difícil de aprovechar. Para llenar este vacío, este estudio tiene como objetivo una revisión bibliográfica de investigaciones que desarrollas estas prácticas de inteligencia de mercado en la empresa contemporáneas. Los principales resultados de la investigación se destacan la utilizando un enfoque de minería de texto, que permite identificar los atributos clave que conducen la satisfacción del cliente o la insatisfacción hacia los productos y servicios.
References
Aad, G., Abbott, B., Abdallah, J., Abdinov, O., Aben, R., Abolins, M., . . . Abreu, R. (2016). Measurements of the Higgs boson production and decay rates and coupling strengths using pp collision data atsqrt {s}= 7 and 8 TeV in the ATLAS experiment. The European Physical Journal C, 76(1), 6.
Berezin, A. E., Kremzer, A. A., Martovitskaya, Y. V., Berezina, T. A., & Gromenko, E. A. (2016). Pattern of endothelial progenitor cells and apoptotic endothelial cell-derived microparticles in chronic heart failure patients with preserved and reduced left ventricular ejection fraction. EBioMedicine, 4, 86-94.
Berezina, K., Bilgihan, A., Cobanoglu, C., & Okumus, F. (2016). Understanding satisfied and dissatisfied hotel customers: text mining of online hotel reviews. Journal of Hospitality Marketing & Management, 25(1), 1-24.
Cambria, E., Schuller, B., Xia, Y., & Havasi, C. (2013). New avenues in opinion mining and sentiment analysis. IEEE Intelligent Systems, 28(2), 15-21.
Cantallops, A. S., & Salvi, F. (2014). New consumer behavior: A review of research on eWOM and hotels. International Journal of Hospitality Management, 36, 41-51.
Chau, M., & Xu, J. (2012). Business intelligence in blogs: Understanding consumer interactions and communities. MIS quarterly, 36(4).
Chen, J., Wiley, B., Li, Z. Y., Campbell, D., Saeki, F., Cang, H., . . . Xia, Y. (2005). Gold nanocages: engineering their structure for biomedical applications. Advanced Materials, 17(18), 2255-2261.
Chowdhary, N., & Prakash, M. (2005). Service quality: revisiting the two factors theory. Journal of Services Research, 5(1), 61.
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts, methods, and analytics. International Journal of Information Management, 35(2), 137-144.
Hassan, A., Mohammed, N., & Azad, A. (2016). Sentiment Analysis on Bangla and Romanized Bangla Text (BRBT) using Deep Recurrent models. arXiv preprint arXiv:1610.00369.
He, W., Zha, S., & Li, L. (2013). Social media competitive analysis and text mining: A case study in the pizza industry. International Journal of Information Management, 33(3), 464-472.
Kassebaum, N. J., Bertozzi-Villa, A., Coggeshall, M. S., Shackelford, K. A., Steiner, C., Heuton, K. R., . . . Dicker, D. (2014). Global, regional, and national levels and causes of maternal mortality during 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. The Lancet, 384(9947), 980-1004.
LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., & Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT sloan management review, 52(2), 21.
Lim, S. S., Vos, T., Flaxman, A. D., Danaei, G., Shibuya, K., Adair-Rohani, H., . . . Andrews, K. G. (2013). A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. The Lancet, 380(9859), 2224-2260.
Lipizzi, C., Iandoli, L., & Marquez, J. E. R. (2015). Extracting and evaluating conversational patterns in social media: A socio-semantic analysis of customers”™ reactions to the launch of new products using Twitter streams. International Journal of Information Management, 35(4), 490-503.
Liu, Y., Xiao, N., Gong, N., Wang, H., Shi, X., Gu, W., & Ye, L. (2014). One-step microwave-assisted polyol synthesis of green luminescent carbon dots as optical nanoprobes. Carbon, 68, 258-264.
Matzler, K., & Sauerwein, E. (2002). The factor structure of customer satisfaction: An empirical test of the importance grid and the penalty-reward-contrast analysis. International journal of service industry management, 13(4), 314-332.
Meire, M., Ballings, M., & Van den Poel, D. (2016). The added value of auxiliary data in sentiment analysis of Facebook posts. Decision Support Systems, 89, 98-112.
Menghi, C., Spoletini, P., & Ghezzi, C. (2017). Integrating goal model analysis with iterative design Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10153 LNCS, pp. 112-128).
Misof, B., Liu, S., Meusemann, K., Peters, R. S., Donath, A., Mayer, C., . . . Beutel, R. G. (2014). Phylogenomics resolves the timing and pattern of insect evolution. Science, 346(6210), 763-767.
Montoyo, A., MartíNez-Barco, P., & Balahur, A. (2012). Subjectivity and sentiment analysis: An overview of the current state of the area and envisaged developments: Elsevier.
Ng, E. K., Li, R., Shin, V. Y., Jin, H. C., Leung, C. P., Ma, E. S., . . . Law, W. (2013). Circulating microRNAs as specific biomarkers for breast cancer detection. PLoS ONE, 8(1), e53141.
Pang, B., & Lee, L. (2008). Opinion mining and sentiment analysis. Foundations and Trends ® in Information Retrieval, 2(1–2), 1-135.
Park, S.-H., Huh, S.-Y., Oh, W., & Han, S. P. (2012). A social network-based inference model for validating customer profile data. MIS quarterly, 36(4).
Russom, P. (2011). Big data analytics. TDWI best practices report, fourth quarter, 19, 40.
Salehan, M., & Kim, D. J. (2016). Predicting the performance of online consumer reviews: A sentiment mining approach to big data analytics. Decision Support Systems, 81, 30-40.
Schuckert, M., Liu, X., & Law, R. (2015). Hospitality and tourism online reviews: Recent trends and future directions. Journal of Travel & Tourism Marketing, 32(5), 608-621.
Sim, J., Mak, B., & Jones, D. (2006). A model of customer satisfaction and retention for hotels. Journal of Quality Assurance in Hospitality & Tourism, 7(3), 1-23.
Somers, S., Van Parys, H., Provoost, V., Buysse, A., Pennings, G., & De Sutter, P. (2017). How to create a family? Decision making in lesbian couples using donor sperm. Sexual and Reproductive Healthcare, 11, 13-18. doi:10.1016/j.srhc.2016.08.005
Xiang, Q., Cheng, B., & Yu, J. (2015). Graphene”Based Photocatalysts for Solar”Fuel Generation. Angewandte Chemie International Edition, 54(39), 11350-11366.
Xiang, Z., Schwartz, Z., Gerdes, J. H., & Uysal, M. (2015). What can big data and text analytics tell us about hotel guest experience and satisfaction? International Journal of Hospitality Management, 44, 120-130.
Yaqoob, P., Pala, H., Cortina-Borja, M., Newsholme, E., & Calder, P. (2000). Encapsulated fish oil enriched in a-tocopherol alters plasma phospholipid and mononuclear cell fatty acid compositions but not mononuclear cell functions. Eur J Clin Invest, 30, 260-274.
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