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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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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