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Helpfulness Prediction for Online Reviews with Explicit Content-Rating Interaction

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Web Information Systems Engineering – WISE 2019 (WISE 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11881))

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Abstract

Automatic helpfulness prediction aims to prioritize online product reviews by quality. Existing methods have combined review content and star ratings for automatic helpfulness prediction. However, the relationship between review content and star ratings is not explicitly captured, which limits the capability of rating information in influencing review content. This paper proposes a deep neural architecture to learn the explicit content-rating interaction (ECRI) for automatic helpfulness prediction. Specifically, ECRI explores two methods to interact review content with star ratings and adaptively specify the amount of rating information needed by review content. ECRI is evaluated against state-of-the-art methods on six real-world domains of the Amazon 5-core dataset. Experimental results demonstrate that exploiting the explicit content-rating interaction improves automatic helpfulness prediction. The source code of ECRI can be obtained from https://github.com/tokawah/ECRI.

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References

  1. Charrada, E.B.: Which one to read? Factors influencing the usefulness of online reviews for re. In: 2016 IEEE 24th International Requirements Engineering Conference Workshops (REW), pp. 46–52, September 2016

    Google Scholar 

  2. Chen, C., et al.: Review helpfulness prediction with embedding-gated CNN. CoRR abs/1808.09896 (2018)

    Google Scholar 

  3. Chen, C., Yang, Y., Zhou, J., Li, X., Bao, F.S.: Cross-domain review helpfulness prediction based on convolutional neural networks with auxiliary domain discriminators. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pp. 602–607. Association for Computational Linguistics, New Orleans, Louisiana, June 2018

    Google Scholar 

  4. Chung, J., Gülçehre, Ç., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. CoRR abs/1412.3555 (2014)

    Google Scholar 

  5. Collobert, R., Weston, J., Bottou, L., Karlen, M., Kavukcuoglu, K., Kuksa, P.: Natural language processing (almost) from scratch. J. Mach. Learn. Res. 12, 2493–2537 (2011)

    Google Scholar 

  6. Danescu-Niculescu-Mizil, C., Kossinets, G., Kleinberg, J., Lee, L.: How opinions are received by online communities: a case study on amazon.com helpfulness votes. In: Proceedings of the 18th International Conference on World Wide Web WWW 2009, pp. 141–150. ACM, New York (2009). https://doi.org/10.1145/1526709.1526729

  7. Dauphin, Y.N., Fan, A., Auli, M., Grangier, D.: Language modeling with gated convolutional networks. In: Proceedings of the 34th International Conference on Machine Learning, vol. 70, pp. 933–941. JMLR.org (2017)

    Google Scholar 

  8. Fan, M., Feng, Y., Sun, M., Li, P., Wang, H., Wang, J.: Multi-task neural learning architecture for end-to-end identification of helpful reviews. In: 2018 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 343–350, August 2018

    Google Scholar 

  9. Ghose, A., Ipeirotis, P.G.: Estimating the helpfulness and economic impact of product reviews: mining text and reviewer characteristics. IEEE Trans. Knowl. Data Eng. 23(10), 1498–1512 (2011)

    Article  Google Scholar 

  10. He, R., McAuley, J.: Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In: Proceedings of the 25th International Conference on World Wide Web. WWW 2016, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, pp. 507–517 (2016). https://doi.org/10.1145/2872427.2883037

  11. Hoffait, A.S., Ittoo, A., Schyns, M.: Assessing and predicting review helpfulness: Critical review, open challenges and research agenda. In: 29ème conférence européenne sur la recherche opérationnelle (EURO2018) (2018)

    Google Scholar 

  12. Hu, H., Li, J., Wang, H., Daggard, G.: Combined gene selection methods for microarray data analysis. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds.) KES 2006. LNCS (LNAI), vol. 4251, pp. 976–983. Springer, Heidelberg (2006). https://doi.org/10.1007/11892960_117

    Chapter  Google Scholar 

  13. Huang, J., Peng, M., Wang, H., Cao, J., Gao, W., Zhang, X.: A probabilistic method for emerging topic tracking in microblog stream. World Wide Web 20(2), 325–350 (2017)

    Article  Google Scholar 

  14. Khalil, F., Li, J., Wang, H.: An integrated model for next page access prediction. IJ Knowl. Web Intell. 1(1/2), 48–80 (2009)

    Article  Google Scholar 

  15. Khalil, F., Wang, H., Li, J.: Integrating markov model with clustering for predicting web page accesses. In: Proceeding of the 13th Australasian World Wide Web Conference (AusWeb 2007), pp. 63–74. AusWeb (2007)

    Google Scholar 

  16. Kim, S.M., Pantel, P., Chklovski, T., Pennacchiotti, M.: Automatically assessing review helpfulness. In: Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing EMNLP 2006, pp. 423–430. Association for Computational Linguistics, Stroudsburg, PA, USA (2006)

    Google Scholar 

  17. Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1746–1751. Association for Computational Linguistics, Doha, Qatar, October 2014

    Google Scholar 

  18. Kim, Y., Jernite, Y., Sontag, D., Rush, A.M.: Character-aware neural language models. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence AAAI 2016, pp. 2741–2749. AAAI Press (2016)

    Google Scholar 

  19. Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. CoRR abs/1412.6980 (2014)

    Google Scholar 

  20. Kozinets, R.V.: Amazonian forests and trees: multiplicity and objectivity in studies of online consumer-generated ratings and reviews, a commentary on de Langhe, Fernbach, and Lichtenstein. J. Consum. Res. 42(6), 834–839 (2016). https://doi.org/10.1093/jcr/ucv090

  21. Krishnamoorthy, S.: Linguistic features for review helpfulness prediction. Expert Syst. Appl. 42(7), 3751–3759 (2015)

    Article  Google Scholar 

  22. Lee, S., Choeh, J.Y.: Predicting the helpfulness of online reviews using multilayer perceptron neural networks. Expert Syst. Appl. 41(6), 3041–3046 (2014)

    Article  Google Scholar 

  23. Li, H., Wang, Y., Wang, H., Zhou, B.: Multi-window based ensemble learning for classification of imbalanced streaming data. World Wide Web 20(6), 1507–1525 (2017)

    Article  Google Scholar 

  24. Li, L., Situ, R., Gao, J., Yang, Z., Liu, W.: A hybrid model combining convolutional neural network with XGBoost for predicting social media popularity. In: Proceedings of the 25th ACM International Conference on Multimedia, pp. 1912–1917. ACM (2017)

    Google Scholar 

  25. Liu, J., Cao, Y., Lin, C.Y., Huang, Y., Zhou, M.: 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. Association for Computational Linguistics, Prague, Czech Republic, June 2007

    Google Scholar 

  26. Ma, J., Sun, L., Wang, H., Zhang, Y., Aickelin, U.: Supervised anomaly detection in uncertain pseudoperiodic data streams. ACM Trans. Internet Technol. (TOIT) 16(1), 4 (2016)

    Article  Google Scholar 

  27. Ma, Y., Xiang, Z., Du, Q., Fan, W.: Effects of user-provided photos on hotel review helpfulness: an analytical approach with deep leaning. Int. J. Hospitality Manage. 71, 120–131 (2018)

    Article  Google Scholar 

  28. Malik, M., Hussain, A.: Helpfulness of product reviews as a function of discrete positive and negative emotions. Comput. Hum. Behav. 73, 290–302 (2017)

    Article  Google Scholar 

  29. Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)

    Google Scholar 

  30. Ocampo Diaz, G., Ng, V.: Modeling and prediction of online product review helpfulness: a survey. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 698–708. Association for Computational Linguistics, Melbourne, Australia, July 2018

    Google Scholar 

  31. Paul, D., Sarkar, S., Chelliah, M., Kalyan, C., Sinai Nadkarni, P.P.: Recommendation of high quality representative reviews in e-commerce. In: Proceedings of the Eleventh ACM Conference on Recommender Systems RecSys 2017, pp. 311–315. ACM, New York (2017). https://doi.org/10.1145/3109859.3109901

  32. Peng, M., Zeng, G., Sun, Z., Huang, J., Wang, H., Tian, G.: Personalized app recommendation based on app permissions. World Wide Web 21(1), 89–104 (2018)

    Article  Google Scholar 

  33. Pennebaker, J.W., Boyd, R.L., Jordan, K., Blackburn, K.: The development and psychometric properties of LIWC2015. Technical report. The University of Texas at Austin (2015)

    Google Scholar 

  34. Qu, X., Li, X., Rose, J.R.: Review helpfulness assessment based on convolutional neural network. CoRR abs/1808.09016 (2018)

    Google Scholar 

  35. Roy, G., Datta, B., Mukherjee, S.: Role of electronic word-of-mouth content and valence in influencing online purchase behavior. J. Market. Commun. 0(0), 1–24 (2018)

    Google Scholar 

  36. Salehan, M., Kim, D.J.: Predicting the performance of online consumer reviews: a sentiment mining approach to big data analytics. Decis. Support Syst. 81, 30–40 (2016)

    Article  Google Scholar 

  37. Scholz, M., Dorner, V.: The recipe for the perfect review? Bus. Inf. Syst. Eng. 5(3), 141–151 (2013)

    Article  Google Scholar 

  38. Schuckert, M., Liu, X., Law, R.: Insights into suspicious online ratings: direct evidence from tripadvisor. Asia Pac. J. Tourism Res. 21(3), 259–272 (2016)

    Article  Google Scholar 

  39. Shin, S., Chung, N., Xiang, Z., Koo, C.: Assessing the impact of textual content concreteness on helpfulness in online travel reviews. J. Travel Res. 58(4), 579–593 (2019)

    Article  Google Scholar 

  40. Siering, M., Muntermann, J.: What drives the helpfulness of online product reviews? from stars to facts and emotions. Wirtschaftsinformatik 7, 103–118 (2013)

    Google Scholar 

  41. Siering, M., Muntermann, J., Rajagopalan, B.: Explaining and predicting online review helpfulness: the role of content and reviewer-related signals. Decis. Support Syst. 108, 1–12 (2018)

    Article  Google Scholar 

  42. Tsang, A.S., Prendergast, G.: Is a “star” worth a thousand words?: The interplay between product-review texts and rating valences. Eur. J. Market. 43(11/12), 1269–1280 (2009)

    Article  Google Scholar 

  43. Wang, K.N., Bell, J.S., Chen, E.Y.H., Gilmartin-Thomas, J.F.M., Ilomäki, J.: Medications and prescribing patterns as factors associated with hospitalizations from long-term care facilities: a systematic review. Drugs Aging 35(5), 423–457 (2018)

    Article  Google Scholar 

  44. Yang, Y., Chen, C., Bao, F.S.: Aspect-based helpfulness prediction for online product reviews. In: 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 836–843, November 2016

    Google Scholar 

  45. Yang, Y., Yan, Y., Qiu, M., Bao, F.: Semantic analysis and helpfulness prediction of text for online product reviews. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pp. 38–44. Association for Computational Linguistics, Beijing, China, July 2015

    Google Scholar 

  46. Zhang, J., Tao, X., Wang, H.: Outlier detection from large distributed databases. World Wide Web 17(4), 539–568 (2014)

    Article  Google Scholar 

  47. Zhang, R., Tran, T., Mao, Y.: Opinion helpfulness prediction in the presence of “words of few mouths”. World Wide Web 15(2), 117–138 (2012)

    Article  Google Scholar 

  48. Zhou, S., Guo, B.: The interactive effect of review rating and text sentiment on review helpfulness. In: Stuckenschmidt, H., Jannach, D. (eds.) EC-Web 2015. LNBIP, vol. 239, pp. 100–111. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-27729-5_8

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Institute of Sustainable Industries and Liveable Cities, Victoria University, Melbourne, VIC, Australia

    Jiahua Du, Jia Rong, Hua Wang & Yanchun Zhang

  2. Faculty of Information Technology, Monash University, Clayton, VIC, Australia

    Jia Rong

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  1. Jiahua Du
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  2. Jia Rong
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  3. Hua Wang
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  4. Yanchun Zhang
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Correspondence to Jia Rong .

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Editors and Affiliations

  1. University of Hong Kong, Hong Kong SAR, China

    Reynold Cheng

  2. University of Ioannina, Ioannina, Greece

    Nikos Mamoulis

  3. University of California, Los Angeles, CA, USA

    Yizhou Sun

  4. Hong Kong Baptist University, Kowloon Tong, Hong Kong SAR, China

    Xin Huang

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Du, J., Rong, J., Wang, H., Zhang, Y. (2019). Helpfulness Prediction for Online Reviews with Explicit Content-Rating Interaction. In: Cheng, R., Mamoulis, N., Sun, Y., Huang, X. (eds) Web Information Systems Engineering – WISE 2019. WISE 2020. Lecture Notes in Computer Science(), vol 11881. Springer, Cham. https://doi.org/10.1007/978-3-030-34223-4_50

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  • DOI: https://doi.org/10.1007/978-3-030-34223-4_50

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