The Challenges of Modeling and Predicting Online Review Helpfulness
Predicting review helpfulness is an important task in Natural Language Processing. It is useful for dealing with the huge amount of online reviews on varied domains and languages, helping and guiding users on what to read and consider in their daily decisions. However, there are limited initiatives to investigate the nature of this task and how hard it is. This paper aims to fulfill this gap, providing a better understanding of it. Two complementary experiments are performed in order to uncover patterns of usefulness evaluation as performed by humans and relevant features for machine prediction. To assure our results, we run the experiments for two different domains: movies and apps. We show that humans agree on the process of assigning helpfulness to reviews, despite the difficulty of the task. More than this, people perform this process systematically and consistently. Finally, we empirically identify the most relevant content features for machine learning prediction of review helpfulness.
Balage Filho, P., Pardo, T. A. S., and Aluísio, S. (2013). An evaluation of the brazilian portuguese liwc dictionary for sentiment analysis. In Proceedings of the 9th Brazilian Symposium in Information and Human Language Technology.
Baowaly, M. K., Tu, Y.-P., and Chen, K.-T. (2019). Predicting the helpfulness of game reviews: A case study on the steam store. Journal of Intelligent & Fuzzy Systems, 36(5):4731–4742.
Diaz, G. O. and Ng, V. (2018). 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), volume 1, pages 698–708.
DuBay, W. H. (2004). The principles of readability. Online Submission.
Ghose, A. and Ipeirotis, P. G. (2011). Estimating the helpfulness and economic impact of product reviews: Mining text and reviewer characteristics. IEEE Transactions on Knowledge and Data Engineering, 23(10):1498–1512.
Hong, Y., Lu, J., Yao, J., Zhu, Q., and Zhou, G. (2012). What reviews are satisfactory: Novel features for automatic helpfulness voting. In Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’12, pages 495–504, New York, NY, USA. ACM.
Huang, A. H., Chen, K., Yen, D. C., and Tran, T. P. (2015). A study of factors that contribute to online review helpfulness. Computers in Human Behavior, 48:17–27.
Kim, S.-M., Pantel, P., Chklovski, T., and Pennacchiotti, M. (2006). Automatically assessing review helpfulness. In Proceedings of the 2006 Conference on empirical methods in natural language processing, pages 423–430. Association for Computational Linguistics.
Krippendorff, K. (1970). Estimating the reliability, systematic error and random error of interval data. Educational and Psychological Measurement, 30(1):61–70.
Liu, J., Cao, Y., Lin, C.-Y., Huang, Y., and 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).
Lu, Y., Tsaparas, P., Ntoulas, A., and Polanyi, L. (2010). Exploiting social context for review quality prediction. In Proceedings of the 19th international conference on World wide web, pages 691–700. ACM.
Mudambi, S. M. and Schuff, D. (2010). Research note: What makes a helpful online review? a study of customer reviews on amazon. com. MIS quarterly, pages 185–200.
Muniz, M. C. M. (2004). A construção de recursos lingüístico-computacionais para o português do Brasil: o projeto Unitex-PB. PhD thesis, Universidade de São Paulo.
Pennebaker, J. W., Francis, M. E., and Booth, R. J. (2001). Linguistic inquiry and word count: Liwc 2001. Mahway: Lawrence Erlbaum Associates, 71(2001):2001.
Silva, M. J., Carvalho, P., and Sarmento, L. (2012). Building a sentiment lexicon for social judgement mining. In International Conference on Computational Processing of the Portuguese Language, pages 218–228. Springer.
Sousa, R. F., Brum, H. B., and Nunes, M. d. G. V. (2019). A bunch of helpfulness and sentiment corpora in brazilian portuguese. In Proceedings of the 12th Brazilian Symposium in Information and Human Language Technology, pages 209–218. Sociedade Brasileira de Computação.
Tsur, O. and Rappoport, A. (2009). Revrank: A fully unsupervised algorithm for selecting the most helpful book reviews. In ICWSM.
Vargas, F. and Pardo, T. (2018). Hierarchical clustering of aspects for opinion mining: a corpus study. In Finatto, M., Rebechi, R., Sarmento, S., and Bocorny, A., editors, Linguística de Corpus: Perspectivas, pages 69–91. Porto Alegre: Instituto de Letras da UFRGS.