[Objective] This paper conducts a fine-grained sentiment analysis of Weibo posts by dividing the sentiments into eight categories and calculating their intensity values. [Methods] First, we analyzed the Weibo corpus to construct the question word list. Besides the seven sentiments defined by DUTIR, we added “suspected” to the list. Then, we used the Pointwise Mutual Information method, the impacts of negative words and the degree adverbs to construct the expression symbol dictionary. We employed Python to retrieve the needed data from Weibo, and applied the jiebaR package to segment the words. Finally, we classified the sentiments and calculated their intensity. [Results] We got the proportion of eight sentiment categories and sentiment intensity of commonly used drugs for diabetes. The Precision values of “angry” and “sad” were the highest (85.73% and 83.05%), while the Recall and F values of “happy” and “like” were the highest (more than 81%). The Precision, Recall and F values of “suspected” were 77.33%, 78.58% and 77.95% respectively. [Limitations] The sentiment dictionary needs to be expanded. [Conclusions] The proposed model could analyze the sentiment of Weibo Posts more effectively than traditional methods.
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