Abstract
Common forms of short text are microblogs, Twitter posts, short product reviews, short movie reviews and instant messages. Sentiment analysis of them has been a hot topic. A highly-accurate model is proposed in this paper for short-text sentiment analysis. The researches target microblog, product review and movie reviews. Words, symbols or sentences with emotional tendencies are proved important indicators in short-text sentiment analysis based on massive users’ data. It is an effective method to predict emotional tendencies of short text using these features. The model has noticed the phenomenon of polysemy in single-character emotional word in Chinese and discusses single-character and multi-character emotional word separately. The idea of model can be used to deal with various kinds of short-text data. Experiments show that this model performs well in most cases.
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Huang, Z., Zhao, Z., Liu, Q., Wang, Z. (2015). An Unsupervised Method for Short-Text Sentiment Analysis Based on Analysis of Massive Data. In: Wang, H., et al. Intelligent Computation in Big Data Era. ICYCSEE 2015. Communications in Computer and Information Science, vol 503. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-46248-5_21
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DOI: https://doi.org/10.1007/978-3-662-46248-5_21
Publisher Name: Springer, Berlin, Heidelberg
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