Abstract
Sentiment analysis is a research hotspot in Nature Language Processing, and high-quality sentiment lexicon plays an important part in sentiment analysis. In this paper, we explore an approach to build a microblog-specific Chinese sentiment lexicon from massive microblog data. In feature learning, in order to enhance the quality of word embedding, we build a neural architecture to train a sentiment-aware word embedding by integrating three kinds of knowledge, including the context words and their composing characters, the polarity of sentences and the polarity of labeled words. Experiments conducted on several public datasets show that in both unsupervised and supervised microblog sentiment classification, the lexicon generated by our approach achieves the state-of-the-art performance compared to several existing Chinese sentiment lexicons and our feature learning method successfully catches both semantics and sentiment information.
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This work was supported by the National Key R&D Program of China (2016YFC0800803).
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Kong, L., Li, C., Ge, J., Yang, Y., Zhang, F., Luo, B. (2018). Construction of Microblog-Specific Chinese Sentiment Lexicon Based on Representation Learning. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11012. Springer, Cham. https://doi.org/10.1007/978-3-319-97304-3_16
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