Avaliação automática da utilidade de reviews usando Redes Neurais Artificiais no corpus do Steam
Resumo
Através de reviews, consumidores podem se comunicar com os fornecedores de produtos e serviços e influenciar a decisão de compra de outros consumidores na Internet. Porém, com o alto número de reviews publicados diariamente, é difícil identificar quais textos devem ser lidos. Como uma solução para esse problema, alguns sites utilizam um sistema de avaliação de reviews baseado no voto dos usuários que embora útil, nem sempre é ideal. Este trabalho propõe um modelo de análise automática da utilidade de reviews online de usuários do Steam, usando Rede Neural Artificial Perceptron Multicamadas. Descobriu-se que certas características de reviews afetam a percepção de utilidade e discutimos aplicações e pesquisas futuras.
Palavras-chave:
Avaliação de Reviews, Redes Neurais Artificiais, Reviews Online
Referências
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Cao, Q., Duan, W., and Gan, Q. (2011). Exploring determinants of voting for the “helpfulness” of online user reviews: A text mining approach. Decision Support Systems, 50(2):511 – 521.
Connors, L., Mudambi, S. M., and Schuff, D. (2011). Is it the review or the reviewer? a multimethod approach to determine the antecedents of online review helpfulness. In In 44th Hawaii International Conference on System Sciences.
da Silva, I. N., Spatti, D. H., and Flauzino, R. A. (2010). Redes Neurais Artificiais: para engenharia e ciências aplicadas, chapter Redes Perceptron Multicamadas.Artliber.
Danescu-Niculescu-Mizil, C., Kossinets, G., Kleinberg, J., and Lee, L. (2009). How opinions are received by online communities: A case study on amazon.com helpfulness votes. 18th International Conference on World Wide Web,.
Forman, C., Ghose, A., and Wiesenfeld, B. (2008). Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets. INFORMS.
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Hu, N., Liu, L., and Zhang, J. (2008). Do online reviews affect product sales? the role of reviewer characteristics and temporal effects. Information Technology and Management Vol. 9 No. 3, pages 201–214.
Kim, S.-M., Pantel, P., Chklovski, T., and Pennacchiotti, M. (2006). Automatically assessing review helpfulness. In Jurafsky, D. and Gaussier, r., editors, EMNLP, pages 423–430. ACL.
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Lee, S. and Choeh, J. Y. (2014). Predicting the helpfulness of online reviews using multilayer perceptron neural networks. Expert Syst. Appl., 41(6):3041–3046.
Li, M., Huang, L., Tan, C.-H., andWei, K.-K. (2013). Helpfulness of online product reviews as seen by consumers: Source and content features. International Journal of Electronic Commerce, 17:101–136.
Otterbacher, J. (2009). ’helpfulness’ in online communities: A measure of message quality. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’09, pages 955–964. ACM.
Schindler, R. and Bickart, B. (2012). Perceived helpfulness of online consumer reviews: The role of message content and style. Journal of Consumer Behaviour, 11:234–243.
Sinha, R. R. and Swearingen, K. (2001). Comparing recommendations made by online systems and friends. In DELOS Workshop: Personalisation and Recommender Systems in Digital Libraries.
Sousa, R. F. d., Rabelo, R. A. L., and Moura, R. S. (2015). A fuzzy system-based approach to estimate the importance of online customer reviews. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).
Squarisi, D. and Salvador, A. (2005). A arte de escrever bem: um guia para jornalistas e profissionais do texto. pages 50–52. Editora Contexto.
Vohs, K. D., Baumeister, R. F., Schmeichel, B. J., Twenge, J. M., Nelson, N. M., and Tice, D. M. (2014). Making choices impairs subsequent self-control: A limited-resource account of decision making, self-regulation, and active initiative. Motivation Science, Vol 1(S), pages 19–42.
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Yoon, Y., Guimaraes, T., and Swales, G. (1994). Integrating artificial neural networks with rule-based expert systems. Decis. Support Syst., 11(5):497–507.
Cao, Q., Duan, W., and Gan, Q. (2011). Exploring determinants of voting for the “helpfulness” of online user reviews: A text mining approach. Decision Support Systems, 50(2):511 – 521.
Connors, L., Mudambi, S. M., and Schuff, D. (2011). Is it the review or the reviewer? a multimethod approach to determine the antecedents of online review helpfulness. In In 44th Hawaii International Conference on System Sciences.
da Silva, I. N., Spatti, D. H., and Flauzino, R. A. (2010). Redes Neurais Artificiais: para engenharia e ciências aplicadas, chapter Redes Perceptron Multicamadas.Artliber.
Danescu-Niculescu-Mizil, C., Kossinets, G., Kleinberg, J., and Lee, L. (2009). How opinions are received by online communities: A case study on amazon.com helpfulness votes. 18th International Conference on World Wide Web,.
Forman, C., Ghose, A., and Wiesenfeld, B. (2008). Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets. INFORMS.
Goldenberg, J., Libai, B., and Muller, E. (2001). Talk of the network: A complex systems look at the underlying process of word-of-mouth. Marketing Letters, Volume 12, Issue 3, pages 211–223.
Hu, N., Liu, L., and Zhang, J. (2008). Do online reviews affect product sales? the role of reviewer characteristics and temporal effects. Information Technology and Management Vol. 9 No. 3, pages 201–214.
Kim, S.-M., Pantel, P., Chklovski, T., and Pennacchiotti, M. (2006). Automatically assessing review helpfulness. In Jurafsky, D. and Gaussier, r., editors, EMNLP, pages 423–430. ACL.
Kim, Y. A. and Srivastava, J. (2007). Impact of social influence in e-commerce decision making. In Proceedings of the Ninth International Conference on Electronic Commerce, ICEC ’07, pages 293–302, New York, NY, USA. ACM.
Kincaid, J. P., Fishburne, R. P., Rogers, R. L., and Chissom, B. S. (1975). Derivation of New Readability Formulas (Automated Readability Index, Fog Count and Flesch Reading Ease Formula) for Navy Enlisted Personnel. Technical report.
Landauer, T., Foltz, P., and Laham, D. (1998). An introduction to latent semantic analysis. Discourse processes, 25:259–284.
Lee, S. and Choeh, J. Y. (2014). Predicting the helpfulness of online reviews using multilayer perceptron neural networks. Expert Syst. Appl., 41(6):3041–3046.
Li, M., Huang, L., Tan, C.-H., andWei, K.-K. (2013). Helpfulness of online product reviews as seen by consumers: Source and content features. International Journal of Electronic Commerce, 17:101–136.
Otterbacher, J. (2009). ’helpfulness’ in online communities: A measure of message quality. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’09, pages 955–964. ACM.
Schindler, R. and Bickart, B. (2012). Perceived helpfulness of online consumer reviews: The role of message content and style. Journal of Consumer Behaviour, 11:234–243.
Sinha, R. R. and Swearingen, K. (2001). Comparing recommendations made by online systems and friends. In DELOS Workshop: Personalisation and Recommender Systems in Digital Libraries.
Sousa, R. F. d., Rabelo, R. A. L., and Moura, R. S. (2015). A fuzzy system-based approach to estimate the importance of online customer reviews. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE).
Squarisi, D. and Salvador, A. (2005). A arte de escrever bem: um guia para jornalistas e profissionais do texto. pages 50–52. Editora Contexto.
Vohs, K. D., Baumeister, R. F., Schmeichel, B. J., Twenge, J. M., Nelson, N. M., and Tice, D. M. (2014). Making choices impairs subsequent self-control: A limited-resource account of decision making, self-regulation, and active initiative. Motivation Science, Vol 1(S), pages 19–42.
Wathen, C. N. and Burkell, J. (2002). Believe it or not: Factors influencing credibility on the web. Journal of the American Society for Information Science and Technology, 53(2):134–144.
Yoon, Y., Guimaraes, T., and Swales, G. (1994). Integrating artificial neural networks with rule-based expert systems. Decis. Support Syst., 11(5):497–507.
Publicado
05/07/2016
Como Citar
BARBOSA, Jardeson L. N.; MOURA, Raimundo S..
Avaliação automática da utilidade de reviews usando Redes Neurais Artificiais no corpus do Steam. In: BRAZILIAN WORKSHOP ON SOCIAL NETWORK ANALYSIS AND MINING (BRASNAM), 2016. , 2016, Porto Alegre.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 43-54.
ISSN 2595-6094.
DOI: https://doi.org/10.5753/brasnam.2016.6442.
