Abstract
Social media texts pose a great challenge to sentiment classification. Existing classification methods focus on exploiting sophisticated features or incorporating user interactions, such as following and retweeting. Nevertheless, these methods ignore user attributes such as age, gender and location, which is proved to be a very important prior in determining sentiment polarity according to our analysis. In this paper, we propose two algorithms to make full use of user attributes: (1) incorporate them as simple features, (2) design a graph-based method to model relationship between tweets posted by users with similar attributes. The extensive experiments on seven movie datasets in Sina Weibo show the superior performance of our methods in handling these short and informal texts.
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Acknowledgments
We thank the three anonymous reviewers for their helpful comments and suggestions. The research work has been funded by the Natural Science Foundation of China under Grant No. 61333018.
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Li, J., Yang, H., Zong, C. (2016). Sentiment Classification of Social Media Text Considering User Attributes. In: Lin, CY., Xue, N., Zhao, D., Huang, X., Feng, Y. (eds) Natural Language Understanding and Intelligent Applications. ICCPOL NLPCC 2016 2016. Lecture Notes in Computer Science(), vol 10102. Springer, Cham. https://doi.org/10.1007/978-3-319-50496-4_52
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DOI: https://doi.org/10.1007/978-3-319-50496-4_52
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