融合社交情感分析的股市预测探究

针对现有股市预测研究中所存在的大众情感度量不够全面的问题，提出了一种基于社交情感分析的股市预测模型. 该模型首先基于异构图模型的证券情感量化方法对社交媒介的数据进行情感分析，得到量化的情感时间序列；然后，基于自组织神经网络模型对情感序列及行情指数序列进行建模，从而对股票指数进行预测. 在国内社交媒介及股市行情数据集上的实验结果表明，本文所建立的模型在预测误差和精度上较BP（Back Propagation）神经网络分别提升了15%和12%，能更好地预测股票指数.

Research on Stock Market Prediction Based on Social Sentiment Analysis

Considering the public sentiment is not comprehensively measured in the existing stock market prediction study, the study proposes a stock market prediction model using social sentiment analysis. First of all, a securities sentiment quantitative method based on heterogeneous graph model is applied for sentiment analysis on social media data, and thus quantified sentiment time sequence is obtained. Secondly, a prediction model based on self-organizing neural network is proposed for the stock index prediction by using sentiment sequence and the quotation index sequence. The experimental results on the domestic stock market and social media data sets show that the proposed model has improved by 15% and 12% over the BP (Back Propagation) neural network in the prediction error and accuracy respectively, which can better predict the stock market.