Abstract
The sentiment analysis of short texts is an important research hotspot in natural language processing. Based on the word features, this paper constructs a binary sentiment dictionary for a Chinese short text corpus using statistical methods. Then we calculate the sentiment value of the dictionary by Word2Vec algorithm and seed words. To evaluate the effectiveness of the dictionary, we manually annotated sentiment of the dictionary and compared with the calculation result. We also compared the performance effects of using different emotional dictionaries for the sentiment classification. The results show that the sentiment collocation dictionary is performed well in the emotional classification of Chinese short texts.
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Acknowledgments
The work was supported by the University Innovative Talent Project of Guangdong Province (2013).
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Zhou, J., Chen, B., Lin, Y. (2017). An Approach to Constructing Sentiment Collocation Dictionary for Chinese Short Text Based on Word2Vec. In: Huang, TC., Lau, R., Huang, YM., Spaniol, M., Yuen, CH. (eds) Emerging Technologies for Education. SETE 2017. Lecture Notes in Computer Science(), vol 10676. Springer, Cham. https://doi.org/10.1007/978-3-319-71084-6_64
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DOI: https://doi.org/10.1007/978-3-319-71084-6_64
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