[Objective] This paper aims to construct a stock market weighted prediction model based on micro-blog sentiment analysis. [Methods] Combining the Baidu index, using the time difference correlation coefficient and the random forest to select the initial keyword of micro-blog search, through the Web crawler to obtain the micro-blog information, using the text mining technology to deal with the micro-blog text, judge the emotional polarity of the micro-blog after the participle, analysis of influence factors on the influence of micro-blog, using information gain to determine the weight of micro-blog. [Results] The tendency of emotional integration is basically consistent with the trend of stock prices, and the result accuracy rate is higher. [Limitations] A better adjustment function for the frequency of words is needed. Feature selection does not take into account the relationships among features. [Conclusions] The empirical results show that the model has good prediction effect.
赵明清,武圣强. 基于微博情感分析的股市加权预测方法研究*[J]. 数据分析与知识发现, 2019, 3(2): 43-51.
Mingqing Zhao,Shengqiang Wu. Research on Stock Market Weighted Prediction Method Based on Micro-blog Sentiment Analysis. Data Analysis and Knowledge Discovery, 2019, 3(2): 43-51.
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