Abstract
Sentiment analysis is a fundamental problem in the field of natural language processing. Existing methods incorporate both semantics of texts and user-level information into deep neural networks to perform sentiment classification of social media documents. However, they ignored the relations between users which can serve as a crucial evidence for classification. In this paper, we propose SRPNN, a deep neural network based model to take user social relations into consideration for sentiment classification. Our model is based on the observation that social relations between users with similar sentiment trends provide important signals for deciding the polarity of words and sentences in a document. To make use of such information, we develop a user trust network based random walk algorithm to capture the sequence of users that have similar sentiment orientation. We then propose a deep neural network model to jointly learn the text representation and user social interaction. Experimental results on two popular real-world datasets show that our model significantly outperforms state-of-the-art methods.
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Acknowledgment
This work was supported by NSFC (91646202), National Key R&D Program of China (SQ2018YFB140235), and the 1000-Talent program.
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Zhao, K., Zhang, Y., Zhang, Y., Xing, C., Li, C. (2019). Learning from User Social Relation for Document Sentiment Classification. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11447. Springer, Cham. https://doi.org/10.1007/978-3-030-18579-4_6
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