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CAPRA: a comprehensive approach to product ranking using customer reviews

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Abstract

Online shopping generates billions of dollars in revenues, including both the physical goods and online services. Product images and associated descriptions are the two main sources of information used by the shoppers to gain knowledge about a product. However, these two pieces of information may not always present the true picture of the product. Images could be deceiving, and descriptions could be overwhelming or cryptic. Moreover, the relative rank of these products among the peers may lead to inconsistencies. Hence, a useful and widely used piece of information is “user reviews”. A number of vendors like Amazon have created whole ecosystems around user reviews, thereby boosting their revenues. However, extracting the relevant and useful information out of the plethora of reviews is not straight forward, and is a very tedious job. In this paper we propose a product ranking system that facilitates the online shopping experience by analyzing the reviews for sentiments, evaluating their usefulness, extracting and weighing different product features and aspects, ranking it among similar comparable products, and finally creating a unified rank for each product. Experiment results show the usefulness of our proposed approach in providing an effective and reliable online shopping experience in comparison with similar approaches.

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Notes

  1. http://www.safehomeproducts.com/shp2/news/news20071211.aspx.

  2. http://www.itl.nist.gov/iad/mig//tests/tdt/.

  3. A simple Java interface for the API available in https://code.google.com/p/google-api-spelling-java/.

References

  1. Aaker DA, Keller KL (1990) Consumer evaluations of brand extensions. J Mark 54(1):27–41

    Article  Google Scholar 

  2. Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw ISDN Syst 30(1):107–117

    Article  MATH  Google Scholar 

  3. Daelemans W, Zavrel J, Van der Sloot K, Van den Bosch A (2003) Timbl: tilburg memory-based learner. Version 4:02-01

  4. Dawar N, Parker P (1994) Marketing universals: consumers’ use of brand name, price, physical appearance, and retailer reputation as signals of product quality. J Mark 58(2):81–95

    Article  Google Scholar 

  5. Ding X, Liu B, Yu PS (2008) A holistic lexicon-based approach to opinion mining. In: Proceedings of the international conference on Web search and web data mining. ACM, New York, pp 231–240

  6. Esuli A, Sebastiani F (2006) Sentiwordnet: a publicly available lexical resource for opinion mining. Proc LREC 6:417–422

    Google Scholar 

  7. Feng Q, Hwang K, Dai Y (2009) Rainbow product ranking for upgrading e-commerce. Internet Comput IEEE 13(5):72–80

    Article  Google Scholar 

  8. Ge SL, Song R (2010) Automated error detection of vocabulary usage in college english writing. In: IEEE/WIC/ACM international conference on Web intelligence and intelligent agent technology (WI-IAT’10), vol 3. IEEE, pp 178–181

  9. Ghose A, Ipeirotis PG (2006) Designing ranking systems for consumer reviews: the impact of review subjectivity on product sales and review quality. In: Proceedings of the 16th annual workshop on information technology and systems. Citeseer, pp 303–310

  10. Hsu CW, Chang CC, Lin CJ et al (2003) A practical guide to support vector classification

  11. Hu M, Liu B (2004) Mining and summarizing customer reviews. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 168–177

  12. Hu N, Zhang J, Pavlou PA (2009) Overcoming the J-shaped distribution of product reviews. Commun ACM 52(10):144–147

    Article  Google Scholar 

  13. Joy CM, Leela S (2013) Review on sentence-level clustering with various fuzzy clustering techniques. Int J Eng 2(12):3510–3513

    Google Scholar 

  14. Kennedy A, Inkpen D (2006) Sentiment classification of movie reviews using contextual valence shifters. Comput Intell 22(2):110–125

    Article  MathSciNet  Google Scholar 

  15. Kim SM, Pantel P, Chklovski T, Pennacchiotti M (2006) Automatically assessing review helpfulness. In: Proceedings of the 2006 conference on empirical methods in natural language processing. Association for Computational Linguistics, pp 423–430

  16. Liu J, Cao Y, Lin CY, Huang Y, Zhou M (2007) Low-quality product review detection in opinion summarization. In: Proceedings of the joint conference on empirical methods in natural language processing and computational natural language learning (EMNLP-CoNLL), pp 334–342

  17. Lu Y, Zhang P, Liu J, Li J, Deng S (2013) Health-related hot topic detection in online communities using text clustering. PloS One 8(2):e56221

  18. Miller GA (1995) Wordnet: a lexical database for english. Commun ACM 38(11):39–41

  19. Miller GA, Beckwith R, Fellbaum C, Gross D, Miller KJ (1990) Introduction to wordnet: an on-line lexical database*. Int J Lexicogr 3(4):235–244

    Article  Google Scholar 

  20. Nadali S, Murad MAA, Kadir RA (2010) Sentiment classification of customer reviews based on fuzzy logic. In: 2010 international symposium in information technology (ITSim), vol 2. IEEE, pp 1037–1044

  21. Narayanan R, Liu B, Choudhary A (2009) Sentiment analysis of conditional sentences. In: Proceedings of the 2009 conference on empirical methods in natural language processing, vol 1. Association for Computational Linguistics, pp 180–189

  22. Pang B, Lee L (2004) A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the 42nd annual meeting on association for computational linguistics. Association for Computational Linguistics, p 271

  23. Pessemier EA (1959) A new way to determine buying decisions. J Mark 24(2):41–46

    Article  Google Scholar 

  24. Polanyi L, Zaenen A (2006) Contextual valence shifters. In: Computing attitude and affect in text: theory and applications. Springer, New York, pp 1–10

  25. Quirk R, Crystal D (1985) A comprehensive grammar of the English language, vol 6. Cambridge Univ. Press, Cambridge

  26. Sauper C, Haghighi A, Barzilay R (2011) Content models with attitude. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies, vol 1. Association for Computational Linguistics, pp 350–358

  27. Schapire RE, Singer Y (2000) Boostexter: a boosting-based system for text categorization. Mach Learn 39(2–3):135–168

    Article  MATH  Google Scholar 

  28. Stanford NLP Group (2005) Stanford parser. Retrieved 12(1):2005

  29. Stoyanov V, Cardie C (2008) Topic identification for fine-grained opinion analysis. In: Proceedings of the 22nd international conference on computational linguistics, vol 1. Association for Computational Linguistics, pp 817–824

  30. Taboada M, Brooke J, Tofiloski M, Voll K, Stede M (2011) Lexicon-based methods for sentiment analysis. Comput Linguist 37(2):267–307

    Article  Google Scholar 

  31. Tian P, Liu Y, Liu M, Zhu S (2009) Research of product ranking technology based on opinion mining. In: Second international conference on intelligent computation technology and automation (ICICTA’09), vol 4. IEEE, pp 239–243

  32. Traylor MB (1981) Product involvement and brand commitment. J Advert Res 21(6):51–56

    Google Scholar 

  33. Turney PD (2002) Thumbs up or thumbs down? Semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th annual meeting on association for computational linguistics. Association for Computational Linguistics, pp 417–424

  34. Wang X, McCallum A, Wei X (2007) Topical n-grams: phrase and topic discovery, with an application to information retrieval. In: Seventh IEEE international conference on data mining (ICDM’07). IEEE, pp 697–702

  35. Wiener E, Pedersen JO, Weigend AS et al (1995) A neural network approach to topic spotting. In: Proceedings of SDAIR-95, 4th annual symposium on document analysis and information retrieval. Citeseer, pp 317–332

  36. Wilson T, Wiebe J, Hoffmann P (2009) Recognizing contextual polarity: an exploration of features for phrase-level sentiment analysis. Comput Linguist 35(3):399–433

    Article  Google Scholar 

  37. Zhang K, Cheng Y, Liao W, Choudhary A (2011) Mining millions of reviews: a technique to rank products based on importance of reviews. In: Proceedings of the 13th international conference on electronic commerce. ACM, New York, p 12

  38. Zhang K, Cheng Y, Xie Y, Honbo D, Agrawal A, Palsetia D, Lee K, Liao W, Choudhary A (2011) SES: sentiment elicitation system for social media data. In: 2011 IEEE 11th international conference on data mining workshops (ICDMW). IEEE, pp 129–136

  39. Zhang K, Narayanan R, Choudhary A (2010) Voice of the customers: mining online customer reviews for product feature-based ranking. In: 3rd workshop on online social networks

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Correspondence to Erfan Najmi.

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Najmi, E., Hashmi, K., Malik, Z. et al. CAPRA: a comprehensive approach to product ranking using customer reviews. Computing 97, 843–867 (2015). https://doi.org/10.1007/s00607-015-0439-8

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