基于微博情感分析的股市加权预测方法研究<sup>*</sup>

doi:10.11925/infotech.2096-3467.2018.0546

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摘要

【目的】构建基于微博情感分析的股市加权预测模型。【方法】结合百度指数, 利用时差相关系数和随机森林选取微博搜索初始关键词, 通过爬虫技术获取微博文本, 利用文本挖掘技术对微博文本作分词处理, 判断分词后的微博情感倾向, 分析影响微博影响力的相关因素, 以信息增益确定微博权重。【结果】微博情感综合倾向与股票价格变化情形几乎一致且预测准确率较高。【局限】词汇频数调整函数有待优化; 选取特征时未考虑各特征之间的关系。【结论】实证结果表明所建模型具有良好的预测效果。

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	赵明清
	武圣强

关键词 ：百度指数, 微博情感, 微博影响力, 股票价格, 预测模型

Abstract：

[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.

Key words： Baidu Index Micro-blog Sentiment Micro-blog Influence Stock Price Prediction Model

收稿日期: 2018-05-15

基金资助:*本文系国家自然科学基金青年项目“基于结构化大数据深度挖掘的非寿险保险公司经营风险模型研究”(项目编号: 61502280)和山东科技大学研究生导师指导能力提升计划立项项目“以统计学一级学科硕士学位授权点为平台的大数据人才培养模式与实践措施研究”(项目编号: KDYC17018)的研究成果之一

引用本文:

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, DOI：10.11925/infotech.2096-3467.2018.0546.

链接本文:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.0546

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