Avaliação de características que influenciam nos votos de utilidade de opiniões sobre serviços em Português
Resumo
Este trabalho apresenta a aplicação de uma metodologia de avaliação de utilidade de opiniões com o objetivo de identificar quais características exercem maior influência na quantidade de votos de utilidade: básicas (ex. nota sobre o produto e/ou serviço, data da publicação), textuais (ex. tamanho das palavras, parágrafos) e semântica (ex. o significado das palavras do texto). A avaliação foi realizada em uma base de dados extraída do site TripAdvisor com opiniões sobre hotéis escritas em português. Resultados mostram que os usuários dão mais atenção a opiniões recentes com notas mais altas para valor e localização do hotel e com notas mais baixas para limpeza e qualidade dos quartos. Textos com valores pequenos para inteligibilidade (mais difíceis) recebem mais votos do que textos com valores grandes de inteligibilidade.
Palavras-chave:
Mineração de opiniões, Utilidade da opinião, Qualidade da opinião
Referências
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J. Liu, Y. Cao, C.-Y. Lin, Y. Huang, and M. Zhou. Low-quality product review detection in opinion summarization. In EMNLP-CoNLL, page 334–342, 2007.
J. Tang, H. Gao, X. Hu, and H. Liu. Context-aware review helpfulness rating prediction. In Proceedings of the 7th ACM conference on Recommender systems, page 1–8. ACM, 2013.
M. P. O’Mahony and B. Smyth. Learning to recommend helpful hotel reviews. In Proceedings of the third ACM conference on Recommender systems, page 305–308. ACM, 2009.
N. Korfiatis, E. Garc´ıa-Bariocanal, and S. S´anchez-Alonso. Evaluating content quality and helpfulness of online product reviews: The interplay of review helpfulness vs. review content. Electronic Commerce Research and Applications, 11(3):205–217, May 2012.
O. Tsur and A. Rappoport. RevRank: a fully unsupervised algorithm for selecting the most helpful book reviews. In ICWSM, 2009.
P. McCullagh. Regression models for ordinal data. Journal of the royal statistical society. Series B (Methodological), pages 109–142, 1980.
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S.-M. Kim, P. Pantel, T. Chklovski, and M. Pennacchiotti. Automatically assessing review helpfulness. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, page 423–430. Association for Computational Linguistics, 2006.
T. K. Landauer, P. W. Foltz, and D. Laham. An introduction to latent semantic analysis. Discourse Processes, 25(2-3):259–284, 1998.
T. K. Landauer, P. W. Foltz, and D. Laham. Introduction to Latent Semantic Analysis, volume 25, pages 259–284. Discourse Processes, 1998.
Y. Liu, X. Huang, A. An, and X. Yu. Modeling and predicting the helpfulness of online reviews. pages 443–452. IEEE, Dec. 2008.
Y. Lu, P. Tsaparas, A. Ntoulas, and L. Polanyi. Exploiting social context for review quality prediction. In Proceedings of the 19th international conference on World wide web, page 691–700. ACM, 2010.
Z. Zhang and B. Varadarajan. Utility scoring of product reviews. In Proceedings of the 15th ACM international conference on Information and knowledge management, page 51–57. ACM, 2006.
A. Ghose and P. G. Ipeirotis. Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Transactions on Knowledge and Data Engineering, 23(10):1498–1512, Oct. 2011.
A. Talwar, R. Jurca, and B. Faltings. Understanding user behavior in online feedback reporting. In Proceedings of the 8th ACM conference on Electronic commerce, page 134–142. ACM, 2007.
Apache. Opennlp, 2011.
B. Liu. Sentiment analysis and opinion mining. Synthesis Lectures on Human Language Technologies, 5(1):1–167, 2012.
B. Pang and L. Lee. Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, page 115–124. Association for Computational Linguistics, 2005.
C. Danescu-Niculescu-Mizil, G. Kossinets, J. Kleinberg, and L. Lee. How opinions are received by online communities: a case study on amazon. com helpfulness votes. In Proceedings of the 18th international conference on World wide web, page 141–150. ACM, 2009.
C. E. Scarton and S. M. Alu´ısio. An´alise da inteligibilidade de textos via ferramentas de processamento de l´ıngua natural: adaptando as m´etricas do coh-metrix para o portuguˆes. Linguam´atica, 2(1):45–61, 2010.
C. Scarton, C. Gasperin, and S. Aluisio. Revisiting the readability assessment of texts in portuguese. In Advances in Artificial Intelligence–IBERAMIA 2010, page 306–315. Springer, 2010.
E. M. F. Barboza and E. M. d. A. Nunes. A inteligibilidade dos websites governamentais brasileiros eo acesso para usu´arios com baixo n´ıvel de escolaridade. Inclus˜ao Social, 2(2):19–33, 2007.
H. Akaike. A new look at the statistical model identification. Automatic Control, IEEE Transactions on, 19(6):716–723, Dec 1974.
J. A. Anderson. Regression and ordered categorical variables. Journal of the Royal Statistical Society. Series B (Methodological), pages 1–30, 1984.
J. Lee. What makes people read an online review? the relative effects of posting time and helpfulness on review readership. Cyberpsychology, Behavior, and Social Networking, 16(7):529–535, July 2013.
J. Liu, Y. Cao, C.-Y. Lin, Y. Huang, and M. Zhou. Low-quality product review detection in opinion summarization. In EMNLP-CoNLL, page 334–342, 2007.
J. Tang, H. Gao, X. Hu, and H. Liu. Context-aware review helpfulness rating prediction. In Proceedings of the 7th ACM conference on Recommender systems, page 1–8. ACM, 2013.
M. P. O’Mahony and B. Smyth. Learning to recommend helpful hotel reviews. In Proceedings of the third ACM conference on Recommender systems, page 305–308. ACM, 2009.
N. Korfiatis, E. Garc´ıa-Bariocanal, and S. S´anchez-Alonso. Evaluating content quality and helpfulness of online product reviews: The interplay of review helpfulness vs. review content. Electronic Commerce Research and Applications, 11(3):205–217, May 2012.
O. Tsur and A. Rappoport. RevRank: a fully unsupervised algorithm for selecting the most helpful book reviews. In ICWSM, 2009.
P. McCullagh. Regression models for ordinal data. Journal of the royal statistical society. Series B (Methodological), pages 109–142, 1980.
Q. Cao, W. Duan, and Q. Gan. Exploring determinants of voting for the “helpfulness” of online user reviews: A text mining approach. Decision Support Systems, 50(2):511–521, Jan. 2011.
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2014. ISBN 3-900051-07-0.
S. C. Deerwester, S. T. Dumais, T. K. Landauer, G. W. Furnas, and R. A. Harshman. Indexing by latent semantic analysis. JASIS, 41(6):391–407, 1990.
S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman. Indexing by latent semantic analysis. JOURNAL OF THE AMERICAN SOCIETY FOR INFORMATION SCIENCE, 41(6):391–407, 1990.
S.-M. Kim, P. Pantel, T. Chklovski, and M. Pennacchiotti. Automatically assessing review helpfulness. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, page 423–430. Association for Computational Linguistics, 2006.
T. K. Landauer, P. W. Foltz, and D. Laham. An introduction to latent semantic analysis. Discourse Processes, 25(2-3):259–284, 1998.
T. K. Landauer, P. W. Foltz, and D. Laham. Introduction to Latent Semantic Analysis, volume 25, pages 259–284. Discourse Processes, 1998.
Y. Liu, X. Huang, A. An, and X. Yu. Modeling and predicting the helpfulness of online reviews. pages 443–452. IEEE, Dec. 2008.
Y. Lu, P. Tsaparas, A. Ntoulas, and L. Polanyi. Exploiting social context for review quality prediction. In Proceedings of the 19th international conference on World wide web, page 691–700. ACM, 2010.
Z. Zhang and B. Varadarajan. Utility scoring of product reviews. In Proceedings of the 15th ACM international conference on Information and knowledge management, page 51–57. ACM, 2006.
Publicado
26/05/2015
Como Citar
MARTINS, Augusto; TACLA, Cesar.
Avaliação de características que influenciam nos votos de utilidade de opiniões sobre serviços em Português. In: SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 11. , 2015, Goiânia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2015
.
p. 147-154.
DOI: https://doi.org/10.5753/sbsi.2015.5811.
