Recomendação de reviews personalizada para donos de estabelecimentos
Resumo
Aplicativos online de avaliação geralmente recomendam as revisões (reviews) mais úteis para os usuários leitores de avaliações. Aqui, introduzimos um novo problema: avaliar a utilidade de uma revisão para o dono de um estabelecimento. Especificamente, propomos o uso de aspectos e sentimentos das revisões, e a geração de um ranking ordenado a partir das mais úteis para o gerenciamento e desenvolvimento do estabelecimento.
Palavras-chave:
Avaliação de estabelecimentos, Revisões úteis, Classificação de revisões
Referências
Bigonha, C., Cardoso, T. N. C., Moro, M. M., Gonçalves, M. A., and Almeida, V. A. F. (2012). Sentiment-based influence detection on twitter. JBCS, 18(3):169–183.
Deepak, A. et al. (2014). The promises and perils of mining github. In MSR.
Ghose, A. and Ipeirotis, P. G. (2011). Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE, pages 1498–1512.
Janyce, W. et al. (1999). Development and use of a gold-standard data set for subjectivity classifications. In ACL, pages 246–253.
Jo, Y. and Oh, A. H. (2011). Aspect and sentiment unification model for online review analysis. In WSDM, pages 815–824.
Liu, B. and Zhang, L. (2012). A Survey of Opinion Mining and Sentiment Analysis. In Mining Text Data, pages 415–463.
Liu, J. et al. (2007). Low-quality product review detection in opinion summarization. In EMNLP-CoNLL.
Lourenço Jr., R. et al. (2014). Economically-efficient sentiment stream analysis. In SIGIR, pages 637–646.
Maroun, L., Moro, M. M., Almeida, J., and da Silva, A. P. C. (2016). Assessing review recommendation techniques under a ranking perspective. In ACM Hypertext.
McAuley, J. and Leskovec, J. (2013). Hidden factors and hidden topics: Understanding rating dimensions with review text. In RecSys, pages 165–172.
O’Mahony, M. P. and Smyth, B. (2009). Learning to recommend helpful hotel reviews. In RecSys.
Pang, B. and Lee, L. (2005). Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In ACL, pages 115–124.
Prado, T. R. P. and Moro, M. M. (2015). POIView: Análise de Tendências a partir de Revisões Online. In SBBD Demo Session, pages 167–172.
Riloff, E. and Wiebe, J. (2003). Learning extraction patterns for subjective expressions. In EMNL, pages 105–112.
Sápiras, L. A. and Becker, K. (2014). Mineração da opinião sobre aspectos de candidatos a eleições em comentários de notícias. In SBBD, pages 117–126.
Tang, J. et al. (2013). Context-aware review helpfulness rating prediction. In RecSys, pages 1–8.
Turney, P. D. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In ACL, pages 417 – 424.
Deepak, A. et al. (2014). The promises and perils of mining github. In MSR.
Ghose, A. and Ipeirotis, P. G. (2011). Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE, pages 1498–1512.
Janyce, W. et al. (1999). Development and use of a gold-standard data set for subjectivity classifications. In ACL, pages 246–253.
Jo, Y. and Oh, A. H. (2011). Aspect and sentiment unification model for online review analysis. In WSDM, pages 815–824.
Liu, B. and Zhang, L. (2012). A Survey of Opinion Mining and Sentiment Analysis. In Mining Text Data, pages 415–463.
Liu, J. et al. (2007). Low-quality product review detection in opinion summarization. In EMNLP-CoNLL.
Lourenço Jr., R. et al. (2014). Economically-efficient sentiment stream analysis. In SIGIR, pages 637–646.
Maroun, L., Moro, M. M., Almeida, J., and da Silva, A. P. C. (2016). Assessing review recommendation techniques under a ranking perspective. In ACM Hypertext.
McAuley, J. and Leskovec, J. (2013). Hidden factors and hidden topics: Understanding rating dimensions with review text. In RecSys, pages 165–172.
O’Mahony, M. P. and Smyth, B. (2009). Learning to recommend helpful hotel reviews. In RecSys.
Pang, B. and Lee, L. (2005). Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales. In ACL, pages 115–124.
Prado, T. R. P. and Moro, M. M. (2015). POIView: Análise de Tendências a partir de Revisões Online. In SBBD Demo Session, pages 167–172.
Riloff, E. and Wiebe, J. (2003). Learning extraction patterns for subjective expressions. In EMNL, pages 105–112.
Sápiras, L. A. and Becker, K. (2014). Mineração da opinião sobre aspectos de candidatos a eleições em comentários de notícias. In SBBD, pages 117–126.
Tang, J. et al. (2013). Context-aware review helpfulness rating prediction. In RecSys, pages 1–8.
Turney, P. D. (2002). Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In ACL, pages 417 – 424.
Publicado
04/10/2016
Como Citar
PRADO, Thiago R. P.; MORO, Mirella M..
Recomendação de reviews personalizada para donos de estabelecimentos. In: SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 31. , 2016, Salvador/BA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2016
.
p. 223-228.
ISSN 2763-8979.
DOI: https://doi.org/10.5753/sbbd.2016.24332.
