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Review selection based on content quality

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Abstract

Consumer-generated reviews have become increasingly important in decision-making processes for customers. Meanwhile, the overwhelming quantity of review data makes it extremely difficult to find useful information from it. A considerable amount of studies have attempted to address this problem by selecting reviews that might be helpful for and preferred by users. However, the performance of existing methods is far from ideal. One reason is because of lacking effective criteria to assess the quality of reviews. In this paper, we propose two novel measures, i.e. feature relevance and feature comprehensiveness, to assess the quality of reviews in terms of review content. A review selection approach is presented to select a set of reviews with high quality based on the two measures. Experiments on real-world review datasets show that our proposed method can assess the review quality effectively to improve the performance of review selection.

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References

  1. Al-Maskari A, Sanderson M, Clough P (2007) The relationship between IR effectiveness measures and user satisfaction. In: Proceedings of the 30th annual international ACM SIGIR conference on research and development in information retrieval, SIGIR ’07. ACM, New York, NY, USA, pp 773–774

  2. Busa-Fekete R, Szarvas G, Elteto T, Kégl B (2012) An apple-to-apple comparison of learning-to-rank algorithms in terms of normalized discounted cumulative gain. In: Proceedings of the 20th European conference on artificial intelligence (ECAI 2012): preference learning: problems and applications in AI workshop, 242, IOS Press

  3. Dong R, Schaal M, O’Mahony MP, Smyth B (2013) Topic extraction from online reviews for classification and recommendation. In: Proceedings of the twenty-third international joint conference on artificial intelligence, IJCAI ’13. AAAI Press, pp 1310–1316

  4. Fan F, Zhao WX, Wen J-R, Xu G, Chang EY (2017) Mining collective knowledge: inferring functional labels from online review for business. Knowl Inf Syst 53:723–747

    Article  Google Scholar 

  5. Fayazi A, Lee K, Caverlee J, Squicciarini A (2015) Uncovering crowdsourced manipulation of online reviews. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, SIGIR ’15. ACM, New York, NY, USA, pp 233–242

  6. Hai Z, Cong G, Chang K, Liu W, Cheng P (2014) Coarse-to-fine review selection via supervised joint aspect and sentiment model. In: Proceedings of the 37th international ACM SIGIR conference on research and development in information retrieval, SIGIR ’14. ACM, New York, NY, USA, pp 617–626

  7. Järvelin K, Kekäläinen J (2002) Cumulated gain-based evaluation of IR techniques. ACM Trans Inf Syst 20:422–446

    Article  Google Scholar 

  8. Jurca R, Garcin F, Talwar A, Faltings B (2010) Reporting incentives and biases in online review forums. ACM Trans Web 4:5:1–5:27

    Article  Google Scholar 

  9. Kim S-M, Pantel P, Chklovski T, Pennacchiotti M (2006) Automatically assessing review helpfulness. In: Proceedings of the 2006 conference on empirical methods in natural language processing, EMNLP ’06, Association for Computational Linguistics, Stroudsburg, PA, USA, pp 423–430

  10. Lappas T, Crovella M, Terzi E (2012) Selecting a characteristic set of reviews. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’12. ACM, New York, NY, USA, pp 832–840

  11. Lau RYK, Song D, Li Y, Cheung TCH, Hao J-X (2009) Toward a fuzzy domain ontology extraction method for adaptive e-learning. IEEE Trans Knowl Data Eng 21:800–813

    Article  Google Scholar 

  12. Li J, Zhan L (2011) Online persuasion: how the written word drives WOM. J Advert Res 51:239–257

    Article  Google Scholar 

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

  14. Liu Y, Huang X, An A, Yu X (2008) HelpMeter: a nonlinear model for predicting the helpfulness of online reviews. In: Proceedings of the IEEE/WIC/ACM international conference on web intelligence and intelligent agent technology, WI-IAT ’08 1, pp 793–796

  15. Liu Y, Huang X, An A, Yu X (2008) Modeling and predicting the helpfulness of online reviews. In: Proceedings of the eighth IEEE international conference on data mining, ICDM ’08, pp 443–452

  16. Long C, Zhang J, Huang M, Zhu X, Li M, Ma B (2014) Estimating feature ratings through an effective review selection approach. Knowl Inf Syst 38:419–446

    Article  Google Scholar 

  17. Long C, Zhang J, Zhut X (2010) A review selection approach for accurate feature rating estimation. In: Proceedings of the 23rd international conference on computational linguistics: posters, COLING ’10, Association for Computational Linguistics, Stroudsburg, PA, USA, pp 766–774

  18. Long C, Zhu X, Li M, Ma B (2008) Information shared by many objects. In: Proceedings of the 17th ACM conference on information and knowledge management. ACM, pp 1213–1220

  19. Modani N, Khabiri E, Srinivasan H, Caverlee J (2015) Creating diverse product review summaries: a graph approach. In: International conference on web information systems engineering. Springer, pp 169–184

  20. Nguyen TS, Lauw HW, Tsaparas P (2015) Review selection using micro-reviews. IEEE Trans Knowl Data Eng 27:1098–1111

    Article  Google Scholar 

  21. O’Mahony MP, Smyth B (2010) Using readability tests to predict helpful product reviews. In: Adaptivity. Personalization and fusion of heterogeneous information, RIAO ’10. Le Centre De Hautes Etudes Internationales Dinformatique Documentaire, Paris, France, France, pp 164–167

  22. Siersdorfer S, Chelaru S, Pedro JS, Altingovde IS, Nejdl W (2014) Analyzing and mining comments and comment ratings on the social web. ACM Trans Web 8:17:1–17:39

    Article  Google Scholar 

  23. Tian N, Xu Y, Li Y, Abdel-Hafez A, Jøsang A (2014) Product feature taxonomy learning based on user reviews. In: Proceedings of the 10th international conference on web information systems and technologies, pp 184–192

  24. Tian N, Xu Y, Li Y, Pasi G (2015) Quality-aware review selection based on product feature taxonomy. In: Asia information retrieval symposium. Springer, pp 68–80

  25. Tsaparas P, Ntoulas A, Terzi E (2011) Selecting a comprehensive set of reviews. In: Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, KDD ’11. ACM, New York, NY, USA, pp 168–176

  26. Vural AG, Cambazoglu BB, Karagoz P (2014) Sentiment-focused web crawling. ACM Trans Web 8:22:1–22:21

    Article  Google Scholar 

  27. Wu Z, Wang Y, Wang Y, Wu J, Cao J, Zhang L (2015) Spammers detection from product reviews: a hybrid model. In: IEEE international conference on data mining (ICDM), pp 1039–1044

  28. Xu N, Liu H, Chen J, He J, Du X (2014) Selecting a representative set of diverse quality reviews automatically. In: Proceedings of the 2014 SIAM international conference on data mining, pp 488–496

  29. Wenzhe Y, Zhang R, He X, Sha C (2013) Selecting a diversified set of reviews. Web technologies and applications. Springer, Berlin, pp 721–733

    Google Scholar 

  30. Xiaohui Y, Liu Y, Huang X, An A (2012) Mining online reviews for predicting sales performance: a case study in the movie domain. IEEE Trans Knowl Data Eng 24:720–734

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computer Science, Queensland University of Technology (QUT), Brisbane, Australia

    Nan Tian, Yue Xu & Yuefeng Li

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  1. Nan Tian
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  2. Yue Xu
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  3. Yuefeng Li
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Correspondence to Yue Xu.

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Tian, N., Xu, Y. & Li, Y. Review selection based on content quality. Knowl Inf Syst 62, 2893–2915 (2020). https://doi.org/10.1007/s10115-020-01474-z

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