[Objective] This paper aims to identify sentiment propensity accurately with the help of a new method based on dependency parsing. [Methods] First, we extracted the sentiment stems of the sentences. Second, we defined sentiment-computing rules. Finally, we calculated sentiment propensity of each sentence. [Results] The proposed method achieved an overall accuracy of 84.46%. The average precision rate and recall rate for bullish class were 82.84% and 87.14% respectively, with an F-measure of 84.94%. In the mean time, bearish class got a precision rate of 86.28%, a recall rate of 81.74% and an F-measure of 83.95%. [Limitations] The proposed method did not consider the relevance among clauses. [Conclusions] The dependency parsing can effectively improve the accuracy of sentiment analysis of textual message from financial forum.
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Lan Qiujun,Liu Wenxing,Li Weikang,Hu Xingye. Sentiment Analysis of Financial Forum Textual Message. New Technology of Library and Information Service, DOI：10.11925/infotech.1003-3513.2016.04.08.
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