Análise comparativa das principais plataformas de reclamações online: implicações para análise de mídia social em negócios
Resumo
Novas formas de relacionamento entre empresas e clientes foram introduzidas através do uso massivo de mídias sociais, e têm transformado a forma com a qual clientes tomam decisões de compras. Esta nova realidade explicitou a importância da análise do conteúdo relacionado a marcas e produtos publicados por consumidores em plataformas de mídias sociais. Nesse sentido, este artigo apresenta uma análise comparativa das duas maiores plataformas de reclamação online no Brasil, o ReclameAqui e o Consumidor.gov. Nas análises são utilizados os conteúdos provenientes de mineração de dados textuais de reclamações de cinco grandes empresas do setor de ecommerce brasileiro, com o objetivo de fornecer uma base para compreender os desafios e oportunidades nas análises destas mídias sociais em negócios. Os resultados mostram que a forma de atuação das empresas nessas plataformas deve ser especifica para cada plataforma, pois existem particularidades, tais como grupo de consumidores, conteúdo e principais tipos de problemas.
Palavras-chave:
Mídias Sociais, Mineração de Dados, Modelagem de Tópicos
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Silva, W., Santana, Á., Lobato, F., and Pinheiro, M. (2017). A Methodology for Community Detection in Twitter. In Proceedings of the International Conference on Web Intelligence, pages 1006–1009.
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Aldous, K. K., An, J., and Jansen, B. J. (2019). View , Like , Comment , Post : Analyzing User Engagement by Topic at 4 Levels across 5 Social Media Platforms for 53 News Organizations. In Proceedings of the Thirteenth International AAAI Conference on Web and Social Media, pages 47–57.
Bahtar, A. Z. and Muda, M. (2016). The Impact of User – Generated Content (UGC) on Product Reviews towards Online Purchasing – A Conceptual Framework. Procedia Economics and Finance, 37:337–342.
Charles-Smith, L. E., Reynolds, T. L., Cameron, M. A., Conway, M., Lau, E. H., Olsen,J. M., Pavlin, J. A., Shigematsu, M., Streichert, L. C., Suda, K. J., and Corley, C. D.(2015). Using social media for actionable disease surveillance and outbreak management: A systematic literature review. PLoS ONE, 10(10):1–20.
Cirqueira, D., Fontes Pinheiro, M., Jacob, A., Lobato, F., and Santana, A. (2018). A Literature Review in Preprocessing for Sentiment Analysis for Brazilian Portuguese Social Media. In2018 IEEE/WIC/ACM International Conference on Web Intelligence(WI), pages 746–749. IEEE.
Constantinides, E. and Holleschovsky, N. I. (2016). Impact of Online Product Reviews on Purchasing Decisions. Proceedings of the 12th International Conference on Web Information Systems and Technologies, pages 271–278.
Damani, O. P. (2013). Improving Pointwise Mutual Information (PMI) by incorpora-ting significant co-occurrence. In CoNLL 2013 - 17th Conference on Computational Natural Language Learning, Proceedings, pages 20–28.
de Almeida, J. S. D. N. G. R. T., Lobato, F. M. F., and Junior, A. F. L. J. (2019). Melhorando Sistemas de Social CRM por meio de Eletronic Word-of-Mouth. Revista Eletrônica de Iniciação Científica em Computação, 17(4).
Fang, A., Macdonald, C., Ounis, I., and Habel, P. (2016). Examining the coherence of the top ranked tweet topics. SIGIR 2016 - Proceedings of the 39th International ACMSIGIR Conference on Research and Development in Information Retrieval, pages 825–828.
Fang, B., Ye, Q., Kucukusta, D., and Law, R. (2016c). Analysis of the perceived value of online tourism reviews: Influence of readability and reviewer characteristics. Tourism Management, 52:498–506.
Flesch, R. F. (1948). A new readability yardstick.The Journal of applied psychology, 323:221–33.
Genc-Nayebi, N. and Abran, A. (2017). A systematic literature review: Opinion mining studies from mobile app store user reviews. Journal of Systems and Software, 125:207–219.
Gómez, M., Rouvoy, R., Monperrus, M., and Seinturier, L. (2015). A Recommender System of Buggy App Checkers for App Store Moderators. Proceedings - 2nd ACM International Conference on Mobile Software Engineering and Systems, MOBILE Soft 2015, pages 1–11.
Hirsch, M., Aggarwal, S., Barker, C., Davis, C. J., and Duffy, J. M. (2017). Googling endometriosis: a systematic review of information available on the Internet.American Journal of Obstetrics and Gynecology, 216(5):451–458.e1.
Kim, A. J. and Johnson, K. K. (2016). Power of consumers using social media: Examining the influences of brand-related user-generated content on Facebook.Computers in Human Behavior, 58:98–108.
Li, D., Zamani, S., Zhang, J., and Li, P. (2019). Integration of Knowledge Graph Embed-ding Into Topic Modeling with Hierarchical. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 940–950.
Lobato, F., Pinheiro, M., and Jr, A. J. (2017). Social CRM: Biggest Challenges to Make it Work in the Real World. Business Information Systems Workshops. BIS 2016. Lecture Notes in Business Information Processing, 263:221–232.
Netrica (2020).TOP ECOMMERCE RANKING REPORTS. Disponível em: https://ecommerce-brasil.rankings.netquest.digital. Acesso em: 27/01/2020 .
Orenga-Roglá, S. and Chalmeta, R. (2016). Social customer relationship management:taking advantage of Web 2.0 and Big Data technologies. SpringerPlus, 5(1).
Othman, I. W., Hasan, H., Tapsir, R., Rahman, N. A., Tarmuji, I., Majdi, S., Masuri,S. A., and Omar, N. (2012). Text readability and fraud detection. In ISBEIA 2012 -IEEE Symposium on Business, Engineering and Industrial Applications, pages 296–301. IEEE.
Porto, J. S., Paiva, T. S. d. S., Amaral, C. L. F., Rebouças, T. N. H., and Silva, R. d. A.(2014). Legibilidade de artigos de um periódico nacional na área de melhoramento vegetal. Cultivando o Saber, 7(2):205–211.
Quattrone, G., Nicolazzo, S., Nocera, A., Quercia, D., and Capra, L. (2018). Is the sharing economy about sharing at all? A linguistic analysis of Airbnb reviews. In 12th International AAAI Conference on Web and Social Media, ICWSM 2018, pages 668–671.
Roy, G., Datta, B., and Mukherjee, S. (2019). Role of electronic word-of-mouth content and valence in influencing online purchase behavior. Journal of Marketing Communications, 25(6):661–684.
Schmäh, M., Wilke, T., and Rossmann, A. (2017). Electronic Word-of-Mouth: A Systematic Literature Analysis. Lecture Notes in Informatics (LNI), page 147.
Silva, W., Santana, Á., Lobato, F., and Pinheiro, M. (2017). A Methodology for Community Detection in Twitter. In Proceedings of the International Conference on Web Intelligence, pages 1006–1009.
Vermeer, S. A., Araujo, T., Bernritter, S. F., and van Noort, G. (2019). Seeing the wood for the trees: How machine learning can help firms in identifying relevant electronic word-of-mouth in social media. International Journal of Research in Marketing, 36(3):1–17.
Vydiswaran, V. G., Romero, D. M., Zhao, X., Yu, D., Gomez-Lopez, I., Lu, J. X., Iott, B.,Baylin, A., Clarke, P., Berrocal, V., Goodspeed, R., and Veinot, T. (2018). “Bacon bacon bacon”: Food-related tweets and sentiment in metro detroit. In 12th International AAAI Conference on Web and Social Media, ICWSM 2018, pages 692–695.
Xiang, Z., Du, Q., Ma, Y., and Fan, W. (2017). A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism. Tourism Management, 58:51–65.
Publicado
30/06/2020
Como Citar
DE SOUSA, Gustavo Nogueira; GUIMARÃES, Isabelle; JACOB JR, Antonio F. L.; LOBATO, Fábio M. F..
Análise comparativa das principais plataformas de reclamações online: implicações para análise de mídia social em negócios. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 9. , 2020, Cuiabá.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 154-165.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2020.11171.
