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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References
Chen, H., Sun, M., Tu, C., Lin, Y., Liu, Z.: Neural sentiment classification with user and product attention. In: EMNLP, pp. 1650–1659 (2016)
Dong, L., Wei, F., Zhou, M., Xu, K.: Adaptive multi-compositionality for recursive neural models with applications to sentiment analysis. In: AAAI, pp. 1537–1543 (2014)
Fouss, F., Pirotte, A., Renders, J., Saerens, M.: Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Trans. Knowl. Data Eng. 19(3), 355–369 (2007)
Golbeck, J.: Computing and applying trust in web-based social networks. Ph.D. thesis, University of Maryland, College Park (2005)
Hu, X., Tang, L., Tang, J., Liu, H.: Exploiting social relations for sentiment analysis in microblogging. In: WSDM, pp. 537–546 (2013)
Jamali, M., Ester, M.: TrustWalker: a random walk model for combining trust-based and item-based recommendation. In: KDD, pp. 397–406 (2009)
Kiritchenko, S., Zhu, X., Mohammad, S.M.: Sentiment analysis of short informal texts. J. Artif. Intell. Res. 50, 723–762 (2014)
Kwak, H., Lee, C., Park, H., Moon, S.B.: What is Twitter, a social network or a news media? In: WWW, pp. 591–600 (2010)
Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents. In: ICML, pp. 1188–1196 (2014)
Li, F., Liu, N.N., Jin, H., Zhao, K., Yang, Q., Zhu, X.: Incorporating reviewer and product information for review rating prediction. In: IJCAI, pp. 1820–1825 (2011)
Li, Z., Wei, Y., Zhang, Y., Yang, Q.: Hierarchical attention transfer network for cross-domain sentiment classification. In: AAAI, pp. 5852–5859. AAAI Press (2018)
Liu, K., Li, W., Guo, M.: Emoticon smoothed language models for Twitter sentiment analysis. In: AAAI (2012)
Luo, L., et al.: Beyond polarity: interpretable financial sentiment analysis with hierarchical query-driven attention. In: IJCAI, pp. 4244–4250 (2018)
Massa, P., Avesani, P.: Trust-aware recommender systems. In: RecSys, pp. 17–24 (2007)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: NIPS, pp. 3111–3119 (2013)
Mishra, A., Dey, K., Bhattacharyya, P.: Learning cognitive features from gaze data for sentiment and sarcasm classification using convolutional neural network. In: ACL, pp. 377–387 (2017)
Mnih, V., Heess, N., Graves, A., Kavukcuoglu, K.: Recurrent models of visual attention. In: NIPS, pp. 2204–2212 (2014)
Pang, B., Lee, L.: Seeing stars: exploiting class relationships for sentiment categorization with respect to rating scales. In: ACL (2005)
Perozzi, B., Al-Rfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: KDD, pp. 701–710 (2014)
Qian, Q., Huang, M., Lei, J., Zhu, X.: Linguistically regularized LSTM for sentiment classification. In: ACL, pp. 1679–1689 (2017)
Qu, L., Ifrim, G., Weikum, G.: The bag-of-opinions method for review rating prediction from sparse text patterns. In: COLING, pp. 913–921 (2010)
Socher, R., et al.: Recursive deep models for semantic compositionality over a sentiment treebank. In: EMNLP, pp. 1631–1642 (2013)
Song, K., Feng, S., Gao, W., Wang, D., Yu, G., Wong, K.: Personalized sentiment classification based on latent individuality of microblog users. In: IJCAI, pp. 2277–2283 (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, pp. 1014–1023 (2015)
Tang, D., Qin, B., Liu, T.: Aspect level sentiment classification with deep memory network. In: EMNLP, pp. 214–224 (2016)
Tang, D., Qin, B., Liu, T., Yang, Y.: User modeling with neural network for review rating prediction. In: IJCAI, pp. 1340–1346 (2015)
Tang, D., Wei, F., Yang, N., Zhou, M., Liu, T., Qin, B.: Learning sentiment-specific word embedding for Twitter sentiment classification. In: ACL, pp. 1555–1565 (2014)
Wang, J., Lin, C., Li, M., Zaniolo, C.: An efficient sliding window approach for approximate entity extraction with synonyms. In: EDBT (2019)
Wang, J., Wang, Z., Zhang, D., Yan, J.: Combining knowledge with deep convolutional neural networks for short text classification. In: IJCAI, pp. 2915–2921 (2017)
Wang, W., Feng, S., Gao, W., Wang, D., Zhang, Y.: Personalized microblog sentiment classification via adversarial cross-lingual multi-task learning. In: EMNLP, pp. 338–348. Association for Computational Linguistics (2018)
Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications, vol. 8. Cambridge University Press, Cambridge (1994)
Wu, F., Huang, Y.: Personalized microblog sentiment classification via multi-task learning. In: AAAI, pp. 3059–3065 (2016)
Wu, Z., Dai, X., Yin, C., Huang, S., Chen, J.: Improving review representations with user attention and product attention for sentiment classification. In: AAAI, pp. 5989–5996. AAAI Press (2018)
Zhang, Y., Wu, J., Wang, J., Xing, C.: A transformation-based framework for KNN set similarity search. IEEE Trans. Knowl. Data Eng. (2019)
Zhao, K., et al.: Modeling patient visit using electronic medical records for cost profile estimation. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds.) DASFAA 2018. LNCS, vol. 10828, pp. 20–36. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-91458-9_2
Zhao, Z., Lu, H., Cai, D., He, X., Zhuang, Y.: Microblog sentiment classification via recurrent random walk network learning. In: IJCAI, pp. 3532–3538. ijcai.org (2017)
Zhu, L., Galstyan, A., Cheng, J., Lerman, K.: Tripartite graph clustering for dynamic sentiment analysis on social media. In: SIGMOD, pp. 1531–1542 (2014)
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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