Abstract
With Western culture and science been widely accepted in China, Traditional Chinese Medicine (TCM) has become a controversial issue. So, it is important to study the public’s sentiment and opinions on TCM. The rapid development of online social network, such as twitter, make it convenient and efficient to sample hundreds of millions of people for the aforementioned sentiment study. To the best of our knowledge, the present work is the first attempt that applies sentiment analysis to the fields of TCM on Sina Weibo (a twitter-like microblogging service in China). In our work, firstly, we collected tweets topics about TCM from Sina Weibo, and labelled the tweets as supporting TCM or opposing TCM automatically based on user tags. Then, a Support Vector Machine classifier was built to predict the sentiment of TCM tweets without tags. Finally, we presented a method to adjust the classifier results. The performance of F-measure attained by our method is 97 %.
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References
Zhao, J., Dong, L., Wu, J. Xu, K.: Moodlens: an emoticon-based sentiment analysis system for Chinese tweets. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1528–1531. ACM Press, Jeju island (2012)
Zhang, H., Yu, H., Xiong, D. Liu, Q.: HHMM-based Chinese lexical analyzer ICTCLAS. In: Proc of SIGHAN Workshop on Chinese Language Processing, pp. 758–759. ACM Press, Sapporo (2003)
Deng, H., Han, J., Li, H., Ji, H., Wang, H., Lu, Y.: Exploring and inferring user - user pseudo-friendship for sentiment analysis with heterogeneous networks. Statistical Analysis and Data Mining 7, 308–321 (2014)
Yang, Y., Pedersen, J.: A comparative study on feature selection in text categorization. In: 14th Int’l Conf. Machine Learning, pp. 412–420. ACM Press, Nashville (1997)
Liu, B.: Sentiment Analysis and Opinion Mining. International Journal 5, 1–167 (2012)
Agarwal, A., Xie, B., Vovsha, I., Rambow, O., Passonneau, R.: Sentiment analysis of Twitter data.In: Proceedings of the Workshop on Languages in Social Media, pp. 620–622. ACL Press, Portland (2011)
Pang, B., Lee, L. Vaithyanathan, S.: Thumbs up? Sentiment classification using machine learning techniques. In: Proceedings of Emnlp, pp. 79–86. ACL Press, Stroudsburg (2002)
Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: Liblinear: a library for large linear classification. Journal of Machine Learning Research 9, 1871–1874 (2008)
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© 2015 Springer International Publishing Switzerland
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Shen, J., Zhu, P., Fan, R., Tan, W., Zhan, X. (2015). Sentiment Analysis Based on User Tags for Traditional Chinese Medicine in Weibo. In: Li, J., Ji, H., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2015. Lecture Notes in Computer Science(), vol 9362. Springer, Cham. https://doi.org/10.1007/978-3-319-25207-0_12
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DOI: https://doi.org/10.1007/978-3-319-25207-0_12
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