Abstract
The vertiginous development of technology and knowledge globalization has generated a high interest on social networks within organizations, where its presence has multiplied exponentially in recent years. That is why, it is proposed in this paper to analyze data extracted from social networks, specifically from Twitter, aiming to obtain different data elements that allow management and analysis of the opinions provided by users on different. This information is very useful for client management and acknowledging preferences of brands and organizations. In particular, this work responds to the following research questions: (1) How does the Twitter user behave in different brands? and (2) How do opinions on the network affect the company’s Twitter profile? In this manuscript, we present the tweet user profile for information analysis via a practical software architecture proposal, which is composed by four layers (extraction of the data source, ETL processes (extraction, transformation, and loading), selection of database, and visualization of the results). The implementation and dashboards of this architecture come from the study case of different types of organizations: banking, telephony, shopping, and supermarkets. The processing of the data corresponds to the extraction of tweets generated by the Twitter users of the organizations. Then, the ETL process is obtained via the useful Spoon from Pentaho Data Integration. The processed data is employed to build the final database, and finally, the generated information is visualized by utilizing dashboards from Qlik Sense Desktop. The results of this study evidence that it is possible to implement a practical architecture to analyze the model information of the Twitter user profile through dashboard; consequently, the organizations can opportunely realize better decisions.
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References
Badr, Y., Mohamed, N., & Mohamed, A. (2018). Recent trends in big data analytics towards more enhanced insurance business models. International Journal of Computer Science and Information Security, 15(12), 39–45.
Cano, J. L. (2007). Business intelligence: competir con información. Madrid: Banesto, Fundación Cultur [i.e. Cultural].
Chae, B. K. (2015). Insights from hashtag #supplychain and Twitter analytics: Considering Twitter and Twitter data for supply chain practice and research. International Journal of Production Economics, 165, 247–259. https://doi.org/10.1016/J.IJPE.2014.12.037
Ghiassi, M., Skinner, J., & Zimbra, D. (2013). Twitter brand sentiment analysis: A hybrid system using n-gram analysis and dynamic artificial neural network. Expert Systems with Applications, 40(16), 6266–6282. https://doi.org/10.1016/J.ESWA.2013.05.057
Ikeda, K., Hattori, G., Ono, C., Asoh, H., & Higashino, T. (2013). Twitter user profiling based on text and community mining for market analysis. Knowledge-Based Systems, 51(1), 35–47. https://doi.org/10.1016/j.knosys.2013.06.020
Jansen, B. J., Zhang, M., Sobel, K., & Chowdury, A. (2009). Twitter power: Tweets as electronic word of mouth. Journal of the American Society for Information Science and Technology, 60(11), 2169–2188. https://doi.org/10.1002/asi.21149
Kurnia, P. F. (2018). Business intelligence model to analyze social media information. Procedia Computer Science, 135, 5–14. https://doi.org/10.1016/J.PROCS.2018.08.144
Lewis, D. D. (1998). Naive (Bayes) at forty: The independence assumption in information retrieval. https://doi.org/10.1007/BFb0026666
Lin, J., & Ryaboy, D. (2013). Scaling big data mining infrastructure. ACM SIGKDD Explorations Newsletter, 14(2), 6. https://doi.org/10.1145/2481244.2481247
Martínez-Cámara, E., Martín-Valdivia, M., Perea-Ortega, J., & López, L. (2011). Técnicas de clasificación de opiniones aplicadas a un corpus en español. In Procesamiento de Lenguaje Natural (Vol. 47).
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
Nigam, K., Lafferty, J., & McCallum, A. (1999). Using maximum entropy for text classification. IJCAI-99 Workshop on Machine Learning for Information Filtering.
Priya, S., Sequeira, R., Chandra, J., & Dandapat, S. K. (2019). Where should one get news updates: Twitter or Reddit. Online Social Networks and Media, 9, 17–29. https://doi.org/10.1016/J.OSNEM.2018.11.001
Saxena, A., Gadhiya, S. (2014). A Survey on frequent pattern mining methods-Apriori, Eclat, FP growth. International Journal of Engineering Development and Research (IJEDR), 2(1). Retrieved from https://www.academia.edu/6435809/A_Survey_on_frequent_pattern_mining_methods-Apriori_Eclat_FP_growth.
Sebastiani, F. (2002). Machine learning in automated text categorization. ACM Computing Surveys, 34(1), 1–47. https://doi.org/10.1145/505282.505283
Sebastiani, F., & Esuli, A. (2006). Determining term subjectivity and term orientation for opinion mining. In Procedings Proceedings of EACL-06, 11th Conference of the Europen Chapter of the Association for Computational Linguistics, 193–200. Retrieved from https://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.60.8645.
StrateBi. (2013). Nuevas Tendencias en Business Intelligence Del Big Data al Social Intelligence. Retrieved from https://www.emotools.com/contents/articulos-y-blogs/stratebinuevas-tendencias-en-business-intelligence/.
Visvizi, A., Jussila, J., Lytras, M. D., Ijäs, M. (2019). Tweeting and mining OECD-related microcontent in the post-truth era: A cloud-based app. Computers in Human Behavior.https://doi.org/10.1016/J.CHB.2019.03.022
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This paper is part of the project supported by “Red Iberoamericana para la Competitividad, Innovación y Desarrollo” (REDCID) project number 616RT0515 in “Programa Iberoamericano de Ciencia y Tecnología para el Desarrollo” (CYTED), and the Catholic University of Maule with the research group in databases TRICAHUE.
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Urrutia, A., Nicolas, C. (2021). Profile Information Analysis of Twitter Social Network. In: León-Castro, E., Blanco-Mesa, F., Gil-Lafuente, A.M., Merigó, J.M., Kacprzyk, J. (eds) Intelligent and Complex Systems in Economics and Business. Advances in Intelligent Systems and Computing, vol 1249. Springer, Cham. https://doi.org/10.1007/978-3-030-59191-5_5
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