Sentiment Analysis based on User Tag for Traditional Chinese Medicine in Weibo
This work addresses public sentiment on TCM in China, which is a controversial issue, but it is incremental as it applies existing sentiment analysis methods to a new domain.
The study tackled sentiment analysis on Traditional Chinese Medicine (TCM) in Sina Weibo by collecting and labeling tweets based on user tags, building an SVM classifier, and adjusting results, achieving an F-measure of 97%.
With the acceptance of Western culture and science, Traditional Chinese Medicine (TCM) has become a controversial issue in China. So, it's important to study the public's sentiment and opinion 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 domain of TCM on Sina Weibo (a twitter-like microblogging service in China). In our work, firstly we collect tweets topic about TCM from Sina Weibo, and label the tweets as supporting TCM and opposing TCM automatically based on user tag. Then, a support vector machine classifier has been built to predict the sentiment of TCM tweets without labels. Finally, we present a method to adjust the classifier result. The performance of F-measure attained with our method is 97%.