Tiradentes no TripAdvisor - O que se fala sobre essa simpática cidade histórica?
Resumo
O turismo é uma área que teve grandes impactos com expansão da internet. Hoje é possível planejar uma viagem de casa, usando somente informações da web. No entanto, os usuários chegaram a um ponto em que a quantidade de dados fornecidos pode ser mais confusa do que esclarecedora, causando um problema chamado de sobrecarga de informações. Assim, este trabalho se concentra reduzir este problema. Para isso, utiliza técnicas de mineração de texto para sumarizar as opiniões dos usuários e para entender o quão próximo ela está da classificação discreta dada por eles para um lugar ou atração no TripAdvisor. Como estudo de caso escolhemos a cidade de Tiradentes, localizada no interior de Minas Gerais, Brasil.
Palavras-chave:
mineração de texto, rede social de turismo, Tripadvisor
Referências
Afzaal, M. and Usman, M. (2015). A novel framework for aspect-based opinion classification for tourist places. In 2015 Tenth International Conference on Digital Information Management (ICDIM), pages 1–9. IEEE.
Afzaal, M., Usman, M., and Fong, A. (2019). Tourism mobile app with aspect-based sentiment classification framework for tourist reviews. IEEE Transactions on Consumer Electronics, 65(2):233–242.
Almeida, R. J. A. (2018). Leia - léxico para inferência adaptada. https://github.com/rafjaa/LeIA.
Barbosa, C. M. G., Felix, L. G. d. S., Xavier, C. R., and Vieira, V. d. F. (2019). A framework for the analysis of information propagation in social networks combining complex networks and text mining techniques. In Proceedings of the 25th Brazillian Symposium on Multimedia and the Web, pages 401–408.
Chang, Y.-C., Ku, C.-H., and Chen, C.-H. (2019). Social media analytics: Extracting and visualizing hilton hotel ratings and reviews from tripadvisor. International Journal of Information Management, 48:263–279.
Eggert, J. and Korner, E. (2004). Sparse coding and nmf. In 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), volume 4, pages 2529–2533 vol.4.
Ghosh, S., Chakraborty, P., Nsoesie, E. O., Cohn, E., Mekaru, S. R., Brownstein, J. S., and Ramakrishnan, N. (2017). Temporal topic modeling to assess associations between news trends and infectious disease outbreaks. Scientific reports, 7:40841.
Greene, D. and Cross, J. P. (2016). Exploring the political agenda of the european parliament using a dynamic topic modeling approach.
Gudivada, V. N. and Arbabifard, K. (2018). Chapter 3 - open-source libraries, application frameworks, and workflow systems for nlp. In Gudivada, V. N. and Rao, C., editors, Computational Analysis and Understanding of Natural Languages: Principles, Methods and Applications, volume 38 of Handbook of Statistics, pages 31 – 50. Elsevier.
Hofmann, T. (1999). Probabilistic latent semantic analysis. In Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence, pages 289–296. Morgan Kaufmann Publishers Inc.
Hutto, C. and Gilbert, E. (2014). Vader: A parsimonious rulebased model for sentiment analysis of social media text. In ICWSM.
Lee, D. D. and Seung, H. S. (2000). Algorithms for non-negative matrix factorization. NIPS’00, page 535–541, Cambridge, MA, USA. MIT Press.
Luiz, W., Viegas, F., Alencar, R., Mourão, F., Salles, T., Carvalho, D., Gonçalves, M. A., and Rocha, L. (2018). A feature-oriented sentiment rating for mobile app reviews. WWW ’18.
Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J. R., Bethard, S., and McClosky, D. (2014). The stanford corenlp natural language processing toolkit. In Proceedings of 52nd annual meeting of the association for computational linguistics: system demonstrations, pages 55–60.
Miguéns, J., Baggio, R., and Costa, C. (2008). Social media and tourism destination: Tripadvisor case study.
Moh, T.-S. and Bhagvat, S. (2012). Clustering of technology tweets and the impact of stop words on clusters. In Annual Southeast Regional Conference, New York, NY, USA. Association for Computing Machinery.
Nguyen, D. Q., Billingsley, R., Du, L., and Johnson, M. (2018). Improving topic models with latent feature word representations. CoRR, abs/1810.06306.
Rocha, L., Mour˜ao, F., Silveira, T., Chaves, R., Sa, G., Teixeira, F., Vieira, R., and Ferreira, R. (2015). Saci: Sentiment analysis by collective inspection on social media content. Web Semantics: Science, Services and Agents on the World Wide Web, 34:27–39.
Schouten, K. and Frasincar, F. (2016). Survey on aspectlevel sentiment analysis. IEEE Transactions on Knowledge & Data Engineering, 28(03):813–830.
Silveira, G. T. d. (2014). Turismo, emprego e renda: o caso da cidade histórica de tiradentes-mg.
Valdivia, A., Luzón, M. V., and Herrera, F. (2017). Sentiment analysis in tripadvisor. IEEE Intelligent Systems, 32(4):72–77.
Yuan, G., Murukannaiah, P. K., Zhang, Z., and Singh, M. P. (2014). Exploiting sentiment homophily for link prediction. In Proceedings of the 8th ACM Conference on Recommender systems, pages 17–24.
Zheng, X., Luo, Y., Sun, L., Zhang, J., and Chen, F. (2018). A tourism destination recommender system using users’ sentiment and temporal dynamics. Journal of Intelligent Information Systems, 51(3):557–578.
Afzaal, M., Usman, M., and Fong, A. (2019). Tourism mobile app with aspect-based sentiment classification framework for tourist reviews. IEEE Transactions on Consumer Electronics, 65(2):233–242.
Almeida, R. J. A. (2018). Leia - léxico para inferência adaptada. https://github.com/rafjaa/LeIA.
Barbosa, C. M. G., Felix, L. G. d. S., Xavier, C. R., and Vieira, V. d. F. (2019). A framework for the analysis of information propagation in social networks combining complex networks and text mining techniques. In Proceedings of the 25th Brazillian Symposium on Multimedia and the Web, pages 401–408.
Chang, Y.-C., Ku, C.-H., and Chen, C.-H. (2019). Social media analytics: Extracting and visualizing hilton hotel ratings and reviews from tripadvisor. International Journal of Information Management, 48:263–279.
Eggert, J. and Korner, E. (2004). Sparse coding and nmf. In 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), volume 4, pages 2529–2533 vol.4.
Ghosh, S., Chakraborty, P., Nsoesie, E. O., Cohn, E., Mekaru, S. R., Brownstein, J. S., and Ramakrishnan, N. (2017). Temporal topic modeling to assess associations between news trends and infectious disease outbreaks. Scientific reports, 7:40841.
Greene, D. and Cross, J. P. (2016). Exploring the political agenda of the european parliament using a dynamic topic modeling approach.
Gudivada, V. N. and Arbabifard, K. (2018). Chapter 3 - open-source libraries, application frameworks, and workflow systems for nlp. In Gudivada, V. N. and Rao, C., editors, Computational Analysis and Understanding of Natural Languages: Principles, Methods and Applications, volume 38 of Handbook of Statistics, pages 31 – 50. Elsevier.
Hofmann, T. (1999). Probabilistic latent semantic analysis. In Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence, pages 289–296. Morgan Kaufmann Publishers Inc.
Hutto, C. and Gilbert, E. (2014). Vader: A parsimonious rulebased model for sentiment analysis of social media text. In ICWSM.
Lee, D. D. and Seung, H. S. (2000). Algorithms for non-negative matrix factorization. NIPS’00, page 535–541, Cambridge, MA, USA. MIT Press.
Luiz, W., Viegas, F., Alencar, R., Mourão, F., Salles, T., Carvalho, D., Gonçalves, M. A., and Rocha, L. (2018). A feature-oriented sentiment rating for mobile app reviews. WWW ’18.
Manning, C. D., Surdeanu, M., Bauer, J., Finkel, J. R., Bethard, S., and McClosky, D. (2014). The stanford corenlp natural language processing toolkit. In Proceedings of 52nd annual meeting of the association for computational linguistics: system demonstrations, pages 55–60.
Miguéns, J., Baggio, R., and Costa, C. (2008). Social media and tourism destination: Tripadvisor case study.
Moh, T.-S. and Bhagvat, S. (2012). Clustering of technology tweets and the impact of stop words on clusters. In Annual Southeast Regional Conference, New York, NY, USA. Association for Computing Machinery.
Nguyen, D. Q., Billingsley, R., Du, L., and Johnson, M. (2018). Improving topic models with latent feature word representations. CoRR, abs/1810.06306.
Rocha, L., Mour˜ao, F., Silveira, T., Chaves, R., Sa, G., Teixeira, F., Vieira, R., and Ferreira, R. (2015). Saci: Sentiment analysis by collective inspection on social media content. Web Semantics: Science, Services and Agents on the World Wide Web, 34:27–39.
Schouten, K. and Frasincar, F. (2016). Survey on aspectlevel sentiment analysis. IEEE Transactions on Knowledge & Data Engineering, 28(03):813–830.
Silveira, G. T. d. (2014). Turismo, emprego e renda: o caso da cidade histórica de tiradentes-mg.
Valdivia, A., Luzón, M. V., and Herrera, F. (2017). Sentiment analysis in tripadvisor. IEEE Intelligent Systems, 32(4):72–77.
Yuan, G., Murukannaiah, P. K., Zhang, Z., and Singh, M. P. (2014). Exploiting sentiment homophily for link prediction. In Proceedings of the 8th ACM Conference on Recommender systems, pages 17–24.
Zheng, X., Luo, Y., Sun, L., Zhang, J., and Chen, F. (2018). A tourism destination recommender system using users’ sentiment and temporal dynamics. Journal of Intelligent Information Systems, 51(3):557–578.
Publicado
31/07/2022
Como Citar
ALVES, Antônio P. S.; FÉLIX, Lucas G. da Silva; BARBOSA, Carlos M. G.; VIEIRA, Vinícius da Fonseca; XAVIER, Carolina Ribeiro.
Tiradentes no TripAdvisor - O que se fala sobre essa simpática cidade histórica?. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 11. , 2022, Niterói.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 145-156.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2022.223293.
