Abstract: At present, there are many studies on the relationship between Weibo sentiment and financial forecast. Most forecasting studies concern excessively on text mining techniques, such as pattern recognition, semantic or sentiment analysis, but neglect the procedure of moods dissemination. We provide an integrated framework, including the semantic mining, information transmission and propagating factors analysis, to predict stock prices more accurately. First, we select several factors in the dissemination process, such as emotional absorption of forwarding, influence of content and poster, release time, etc. to optimize the fitting effect of original model. Second, we classify users into two categories, verified or unverified users. And we also take the count of forwarding into account, checking its effect on stock prices fluctuation. Third, we compare the fitting effect of prediction models for different periods of the stock curve. Given a certain keyword related to financial market, we collected over 500,000 Micro-blogs and their user information from Weibo. Experiments demonstrate that our proposed integrated framework outperformed the simple neural network method. We observe that user category and the count of forwarding differ on the lag phase of influence. And more, we found that the model fitting effect were the best in the rising periods of stock prices curves, the second place in the declining and the worst in the fluctuating.

Key words: sentiment mining, stock, Weibo, prediction, propagation effect