Opinion mining framework applied to a social networks data for small and medium enterprises

Huoston Rodrigues Batista, Marcos Antonio Gaspar, Renato José Sassi


2) Objective: To present a framework for the mining of opinions that can be applied in the discovery of knowledge of the customers about to their experiences, based on unstructured data extracted from social networks, and that is applicable to the reality of small and medium enterprises.

3) Methodology: This experimental research accessed data from the opinions of customers of four restaurants published in the social network TripAdvisor Brazil. The framework was based on the proposals formulated by Aranha (2007) and Feldman and Sanger (2007), techniques for Sentiment Analysis by Liu (2012) and Pang and Lee (2008) and Topic Modeling by Blei et al. (2012).

4) Originality: The relevance consists in proposing a solution that is both accessible to SMEs and capable of processing opinions in Portuguese, something not very common in literature. Almost all similar applications in literature are dedicated to the English language.

5) Main results: We highlight the generation of summaries and graphic visualizations that contribute to evidence knowledge about the relations between several expressions and terms that were not obvious. These allowed finding latent relationships between terms cited by different customers.

6) Theoretical contributions: The methodological solution uses efficient and state-of-the-art techniques and methods to extract, process, and analyze customer opinions on the Internet quickly, efficiently, and economically.

7) Social contributions: the framework developed presents an efficient, fast and economical way to mine data, presenting the results of the discovery of customer knowledge through the use of Sentiment Analysis and Topic Modeling techniques.


Data mining; Text mining; Opinion mining; Social networks; Customer knowledge.

Texto completo:



Ammirato, S., Felicetti, A. M., Gala, M. D., Aramo-Immonen, H., Jussila, J. J., & Kärkkäinen, H. (2019). The use of social media for knowledge acquisition and dissemination in B2B companies: an empirical study of Finnish technology industries. Knowledge Management Research & Practice, 17(1), 52–69. https://doi.org/10.1080/14778238.2018.1541779

Aranha, C. N. (2007). Processamento automático para mineração de textos em português: sob o enfoque da inteligência computacional. Rio de Janeiro: PUC-Rio.

Behringer, N., Sassenberg, K., & Scholl, A. (2017). Knowledge contribution in organizations via social media. Journal of Personnel Psychology, 16(1), pp.12-24.

Berry, M. W., & Kogan, J. (Eds.). (2010). Text mining: applications and theory. Chichester, U.K: Wiley.

Blei, D. M. (2012). Probabilistic topic models. Communications of the ACM, 55(4), 77. https://doi.org/10.1145/2133806.2133826

Campbell, A. J. (2003). Creating customer knowledge competence: managing customer relationship management programs strategically. Industrial Marketing Management, 32(5), 375–383.

Casey, S. (2017). The 2016 Nielsen social media report. Retrieved from The Nielsen Company website: http://www.nielsen.com/us/en/insights/reports/2017/2016-nielsen-social-media-report.html

Chierici, R., Mazzucchelli, A., Garcia-Perez, A., & Vrontis, D. (2019). Transforming big data into knowledge: the role of knowledge management practice. Management Decision, 57(8), pp.1902-1922.

Chowdhury, G. G. (2003). Natural language processing. Annual Review of Information Science and Technology, 37(1), 51–89. https://doi.org/10.1002/aris.1440370103

Clark, A., Fox, C., & Lappin, S. (Eds.). (2010). The handbook of computational linguistics and natural language processing. Chichester, West Sussex ; Malden, MA: Wiley-Blackwell.

Correa, T., Hinsley, A. W., & Zúñiga, H. G. de. (2010). Who interacts on the Web?: The intersection of users’ personality and social media use. Computers in Human Behavior, 26(2), 247–253. https://doi.org/10.1016/j.chb.2009.09.003

Crammond, R., & Murray, A. (2018). Managing knowledge through social media. Baltic Journal of Management, 13(3), pp.303-328.

Dalkir, K., & Liebowitz, J. (2011). Knowledge Management in Theory and Practice (second edition). Cambridge, Mass: The MIT Press.

Darroch, J., & McNaughton, R. (2003). Beyond market orientation: Knowledge management and the innovativeness of New Zealand firms. European Journal of Marketing, 37(3/4), 572–593. https://doi.org/10.1108/03090560310459096

Davenport, T. H., & Prusak, L. (2000). Working Knowledge: How Organizations Manage What They Know (2nd edition). Boston, Mass: Harvard Business Review Press.

Esposito, E., & Evangelista, P. (2016). Knowledge management in SME networks. Knowledge Management Research & Practice, 14(2), 204–212. https://doi.org/10.1057/kmrp.2015.18

Feldman, R., & Sanger, J. (2007). The text mining handbook: advanced approaches in analyzing unstructured data. Retrieved from http://www.books24x7.com/marc.asp?bookid=23164

García-Murillo, M., & Annabi, H. (2002). Customer Knowledge Management. The Journal of the Operational Research Society, 53(8), 875–884. https://doi.org/10. 1057/palgravejors.2601365

Hoffman, D. L., & Fodor, M. (2010). Can you measure the ROI of your social media marketing? MIT Sloan Management Review, 52(1), 41.

Hofmann, M., & Chisholm, A. (2013). Text Mining and Visualization - Case Studies Using Open-Source Tools. Boca Raton, FL: Taylor & Francis Group.

Kaletka, C., & Pelka, B. (2011). Web 2.0 revisited: user-generated content as a social innovation. International Journal of Innovation and Sustainable Development, 5(2–3), 264–275.

Kaplan, A. M., & Haenlein, M. (2010). Users of the world, unite! The challenges and opportunities of Social Media. Business Horizons, 53(1), 59–68. https://doi.org/10.1016/j.bushor.2009.09.003

Kietzmann, J. H., Hermkens, K., McCarthy, I. P., & Silvestre, B. S. (2011). Social media? Get serious! Understanding the functional building blocks of social media. Business Horizons, 54(3), 241–251. https://doi.org/10.1016/j.bushor.2011.01.005

Lin, H.-F. (2014). Contextual factors affecting knowledge management diffusion in SMEs. Industrial Management & Data Systems, 114(9), 1415–1437. https://doi.org/10.1108/IMDS-08-2014-0232

Liu, B. (2012). Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1), 1–167.

Medhat, W., Hassan, A., & Korashy, H. (2014). Sentiment analysis algorithms and applications: A survey. Ain Shams Engineering Journal, 5(4), 1093–1113. https://doi.org/10.1016/j.asej.2014.04.011

Mamorobela, S., & Buckley, S. (2018). Evaluating the effectiveness of social media on knowledge management systems for SMEs. European Conference on Knowledge Management, pp.1064-1072

Manivannan, P., & Selvi, C. (2019). Pairwise relative ranking technique for efficient opinion mining using sentiment analysis. Cluster Computing, 22(Supplement 6), pp.13487-13497.

Meneghello, J., Thompson, N., Lee, K., Wong, K., & Abu-Salih, B. (2019). Unlocking social media and user generated content as a data source for knowledge management. Pacrepositoryorg, 13(1), pp. 1-23.

Nejatian, H., Sentosa, I., Piaralal, S. K., & Bohari, A. M. (2011). The Influence of Customer Knowledge on CRM Performance of Malaysian ICT Companies: A Structural Equation Modeling Approach. International Journal of Business and Management, 6(7). https://doi.org/10.5539/ijbm.v6n7p181

Nisar, T. M., Prabhakar, G., & Strakova, L. (2019). Social media information benefits, knowledge management and smart organizations. Journal of Business Research, 94, pp.264-272.

Nonaka, I., & Takeuchi, H. (1995). The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation (1st ed.). Oxford University Press.

O’reilly, T. (2005). What Is Web 2.0. Retrieved September 5, 2015, from http://www.oreilly.com/pub/a/web2/archive/what-is-web-20.html

Pang, B., & Lee, L. (2008). Opinion Mining and Sentiment Analysis. Found. Trends Inf. Retr., 2(1–2), 1–135. https://doi.org/10.1561/1500000011

Paquette, S. (2011). Customer Knowledge Management. In Encyclopedia of knowledge management. Hershey, PA: Idea Group Reference.

Polanyi, M. (1966). The Tacit Dimension. Retrieved from https://books.google.com.br/books?id=zfsb-eZHPy0C

Rollins, M., & Halinen, A. (2005). Customer knowledge management competence: Towards a theoretical framework. Proceedings of the 38th Annual Hawaii International Conference on System Sciences, 240a–240a. Retrieved from http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1385729

Rowley, J. (2002). Eight questions for customer knowledge management in e‐business. Journal of Knowledge Management, 6(5), 500–511. https://doi.org/10.1108/13673270210450441

Sebrae. (2014). Participação das micro e pequenas empresas. Retrieved September 20, 2016, from www.sebrae.com.br website: http://www.sebrae.com.br/

Silva, L. A. da, Peres, S. M., & Boscarioli, C. (2017). Introdução à Mineração de Dados: Com Aplicações em R. Retrieved from https://books.google.com.br/books?id=5LA4DwAAQBAJ

Silva, M. J., Carvalho, P., Costa, C., & Sarmento, L. (2010). Automatic Expansion of a Social Judgment Lexicon for Sentiment Analysis (No. TR 10-08). Retrieved from University of Lisbon, Faculty of Sciences, LASIGE website: http://hdl.handle.net/10455/6694

Souza, M., & Vieira, R. (2012). Sentiment Analysis on Twitter Data for Portuguese Language. In H. Caseli, A. Villavicencio, A. Teixeira, & F. Perdigão (Eds.), Computational Processing of the Portuguese Language (Vol. 7243, pp. 241–247). https://doi.org/10.1007/978-3-642-28885-2_28

Stewart, T. A. (1997). Intellectual Capital: The New Wealth of Organizations. Doubleday / Currency.

Taboada, M., Brooke, J., Tofiloski, M., Voll, K., & Stede, M. (2011). Lexicon-based methods for sentiment analysis. Computational Linguistics, 37(2), 267–307.

Wickham, H. (2014). Tidy data. Journal of Statistical Software, 59(10), 1–23.

DOI: https://doi.org/10.20397/2177-6652/2020.v20i3.1887

Métricas do artigo

Carregando Métricas ...

Metrics powered by PLOS ALM


  • Não há apontamentos.

Direitos autorais 2020 Revista Gestão & Tecnologia

Licença Creative Commons
Esta obra está licenciada sob uma licença Creative Commons Atribuição - NãoComercial 4.0 Internacional.