%A Lan Qiujun,Liu Wenxing,Li Weikang,Hu Xingye %T Sentiment Analysis of Financial Forum Textual Message %0 Journal Article %D 2016 %J Data Analysis and Knowledge Discovery %R 10.11925/infotech.1003-3513.2016.04.08 %P 64-71 %V 32 %N 4 %U {https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/abstract/article_4213.shtml} %8 2016-04-25 %X

[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.