Serviços Personalizados
Journal
Artigo
Indicadores
- Citado por SciELO
- Acessos
Links relacionados
- Similares em SciELO
Compartilhar
Polibits
versão On-line ISSN 1870-9044
Polibits no.39 México Jan./Jun. 2009
Articles
Mining Reviews for Product Comparison and Recommendation
Jianshu Sun, Chong Long, Xiaoyan Zhu, and Minlie Huang
State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China. (email: bigtree2005@gmail.com, longc05@mails.tsinghua.edu.cn, zxydcs@tsinghua.edu.cn, aihuang@tsinghua.edu.cn).
Manuscript received November 5, 2008.
Manuscript accepted for publication February 19, 2009.
Abstract
Recently, as the amount of customer reviews grows rapidly on product service websites, it costs customers much time to select and compare their favorite products. Researchers have been aware of this problem and many studies are investigated to mine the opinions from the online reviews. Unfortunately, few previous works give comparisons or recommendations among the products. In this paper, we propose an automated system to address this problem. We first build a product feature sentiment database from the reviews. Then we perform the comparison among various products from both subjective and objective perspectives on the feature level. Finally, product recommendations can be suggested according to the previous comparisons and an evolution tree constructed from the reviews. Experiment results demonstrate the effectiveness of the proposed approach in mining the digital camera reviews. And now a demo system is put in to practical use.
Key words: Review mining, comparison, recommendation, evolution tree.
DESCARGAR ARTÍCULO EN FORMATO PDF
ACKNOWLEDGMENT
The work was supported by NSFC under grant No.60621062 and 60803075, the National Basic Research Program (973 project in China) under grant No.2007CB311003, and Microsoft joint project "Opinion Summarization toward Opinion Search". The work was also supported by a grant from the International Development Research Center, Canada.
REFERENCES
[1] Bing Liu, Minqing Hu and Junsheng Cheng, "Opinion Observer: Analyzing and comparing opinions on the web," in Proceedings of WWW2005, pp. 342351, 2005. [ Links ]
[2] Minqing Hu and Bing Liu, "Mining and summarizing customer reviews," in Proceedings of ACMKDD 2004, pp. 168177, 2004. [ Links ]
[3] Nitin Jindal and Bing Liu, "Identifying comparative sentences in text documents," in Proc. of SIGIR06, pp. 244251, 2006. [ Links ]
[4] Nitin Jindal and Bing Liu, "Mining comparative sentences and relations," in Proc. of AAAI'06, pp. 244251, 2006. [ Links ]
[5] Li Zhuang, Feng Jing, and Xiaoyan Zhu, "Movie review mining and summarization," in Proc. of CIKM, pp. 4350, 2006. [ Links ]
[6] AnaMaria Popescu and Oren Etzioni, "Extracting product features and opinions from review," in Proc. of EMNLP05, pp. 339346, 2005. [ Links ]
[7] Bo Pang and Lillian Lee, "Seeing stars: Exploiting class relationships for sentiment categorization with respect to rating scales," in Proc. of ACL 2005, pp. 115124, 2005. [ Links ]
[8] Christopher Scaffidi, Kevin Bierhoff, Eric Chang, Mikhael Felker, Herman Ng and Chun Jin, "Red Opal: ProductFeature Scoring from Reviews," in Proc. of ACM EC, pp. 182191, 2007. [ Links ]
[9] Yi Zhang and Jonathan Koren, "Efficient Bayesian hierarchical user modeling for recommendation systems," in Proc. of SiGiR07, pp. 47-54, 2007. [ Links ]
[10] Andrea Esuli and Fabrizio Sebastiani, "Sentiwordnet: A publicly available lexical resource for opinion mining," in Proceedings of LREC 2006, pp. 417422, 2006. [ Links ]
[11] Yiming Yang and Jan O. Pedersen, "A comparative study on feature selection in text categorization," in Proc. of international Conference on Machine Learning (iCML), pp. 412420, 1997. [ Links ]
[12] MarieCatherine de Marneffe, Bill MacCartney and Christopher D. Manning, "Generating typed dependency parses from phrase structure parses," in Proc. of LREC 2006, 2006. [ Links ]
[13] Stanford Parser, http://wwwnlp.stanford.edu/software/lexparser.shtml [ Links ]
[14] SentParBreaker, http://text0.mib.man.ac.uk:8080/scottpiao/sent_detector [ Links ]
[15] Amazon, http://www.amazon.com [ Links ]
[16] Epinions, http://www.epinions.com [ Links ]
[17] Cnet, http://www.cnet.com [ Links ]