Abstract
Neural network methods have achieved promising results for document-level sentiment classification. Since the popularity of Web 2.0, a growing number of websites provide users with voting and feedback systems (or called social feedback system). However, most existing sentiment classification models only focus on text information while ignoring the social feedback signals from fellow users, despite the association between voting and review predicting. To address this issue, first, we conduct empirical analysis based on a large-scale review dataset to verify the relevance between the social feedback signals and the review predicting. Afterward, we build a hierarchical attention model to generate sentence-level and document-level representations. Finally, we feed the social feedback information into word level and sentence level attention layers. Extensive experiments demonstrate that our model can significantly outperform several strong baseline methods and social feedback signals can promote the performance of attention model.
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Notes
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We choose three as the threshold to control for noise and weak social feedback as a result of comparative experiments (see Sect. 4.3).
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References
Freedman, S., Jin, G.Z.: The information value of online social networks: lessons from peer-to-peer lending. Int. J. Ind. Organ. 51, 185–222 (2017)
Bakhshi, S., Kanuparthy, P., Shamma, D.A.: Understanding online reviews: funny, cool or useful? In: CSCW, pp. 1270–1276. ACM (2015)
Bakhshi, S., Kanuparthy, P., Shamma, D.A.: If it is funny, it is mean: understanding social perceptions of yelp online reviews. In: GROUP, pp. 46–52. ACM (2014)
Archak, N., Ghose, A., Ipeirotis, P.G.: Show me the money!: deriving the pricing power of product features by mining consumer reviews. In: KDD, pp. 56–65. ACM (2007)
Bakhshi, S., Kanuparthy, P., Gilbert, E.: Demographics, weather and online reviews: a study of restaurant recommendations. In: WWW, pp. 443–454. ACM (2014)
Lu, Y., Zhai, C., Sundaresan, N.: Rated aspect summarization of short comments. In: WWW, pp. 131–140. ACM (2009)
Danescu-Niculescu-Mizil, C., Kossinets, G., Kleinberg, J.M., Lee, L.: How opinions are received by online communities: a case study on amazon.com helpfulness votes. In: WWW, pp. 141–150. ACM (2009)
Liu, B.: Sentiment Analysis and Opinion Mining. Synthesis Lectures on Human Language Technologies. Morgan & Claypool Publishers, San Rafael (2012)
Kim, Y.: Convolutional neural networks for sentence classification. In: EMNLP, pp. 1746–1751, ACL (2014)
Zhang, X., Zhao, J.J., LeCun, Y.: Character-level convolutional networks for text classification. In: NIPS, pp. 649–657 (2015)
Tang, D., Qin, B., Liu, T.: Document modeling with gated recurrent neural network for sentiment classification. In: EMNLP, pp. 1422–1432. The Association for Computational Linguistics (2015)
Tang, D., Qin, B., Liu, T.: Learning semantic representations of users and products for document level sentiment classification. In: ACL (1), pp. 1014–1023. The Association for Computer Linguistics (2015)
Yang, Z., Yang, D., Dyer, C., He, X., Smola, A.J., Hovy, E.H.: Hierarchical attention networks for document classification. In: HLT-NAACL, pp. 1480–1489. The Association for Computational Linguistics (2016)
Laurent, C., Pereyra, G., Brakel, P., Zhang, Y., Bengio, Y.: Batch normalized recurrent neural networks. In: ICASSP, pp. 2657–2661. IEEE (2016)
Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: ICML. JMLR Workshop and Conference Proceedings, vol. 37, pp. 448–456. JMLR.org (2015)
Zeiler, M.D.: ADADELTA: an adaptive learning rate method. CoRR abs/1212.5701 (2012)
Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents. In: ICML. JMLR Workshop and Conference Proceedings, vol. 32, pp. 1188–1196. JMLR.org (2014)
Grave, E., Mikolov, T., Joulin, A., Bojanowski, P.: Bag of tricks for efficient text classification. In: EACL (2), pp. 427–431. Association for Computational Linguistics (2017)
Acknowledgements
This work was partially supported by the National Natural Science Foundation of China (No. 61332018), and SKLSDE project under Grant No. SKLSDE-2017ZX.
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Wang, T., Ouyang, Y., Rong, W., Xiong, Z. (2018). Neural Sentiment Classification with Social Feedback Signals. In: Liu, W., Giunchiglia, F., Yang, B. (eds) Knowledge Science, Engineering and Management. KSEM 2018. Lecture Notes in Computer Science(), vol 11061. Springer, Cham. https://doi.org/10.1007/978-3-319-99365-2_7
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