Research on stock price prediction using TextRank based text summarization technology and sentiment analysis

News sentiment analysis is widely used in stock price forecasting, and the existing research is mostly limited to sentiment mining of news headlines, ignoring the effective information contained in article news. This study introduces extractive text summarization technology into stock price prediction, use Word2Vec and TextRank algorithm to extract effective text information contained in article news, and adopt the emotion comprehensive calculation method based on news headlines and news abstracts, taking into account the effective information of headlines and original texts. Input the comprehensively calculated news sentiment value as sentiment feature into the stock price prediction model LSTM, and propose a stock price prediction framework based on TextRank text summarization techniques and sentiment analysis. Finally, select the stock transaction data of A-share CTG DUTY-FREE for a total of 587 trading days from December 25, 2019 to May 31, 2022 for comparative experiments. The result shows, the sentiment analysis algorithm based on TextRank text summarization technology proposed in this article has the best extraction effect on the sentiment value of news texts, in terms of prediction accuracy, the model is 11.67% higher than the benchmark model on the test set.