Deep learning based Chinese text sentiment mining and stock market correlation research

TL;DR

This paper uses deep learning, specifically BERT, to analyze financial forum texts and predict stock market indices, demonstrating that sentiment features derived from text can improve market prediction accuracy and inform policy decisions.

Contribution

It introduces a novel application of BERT-based sentiment analysis on financial texts to enhance stock market prediction and understand investor sentiment mechanisms.

Findings

BERT-based sentiment features reflect stock market fluctuations.

Sentiment analysis improves prediction accuracy.

The approach aids policy development for market stability.

Abstract

We explore how to crawl financial forum data such as stock bars and combine them with deep learning models for sentiment analysis. In this paper, we will use the BERT model to train against the financial corpus and predict the SZSE Component Index, and find that applying the BERT model to the financial corpus through the maximum information coefficient comparison study. The obtained sentiment features will be able to reflect the fluctuations in the stock market and help to improve the prediction accuracy effectively. Meanwhile, this paper combines deep learning with financial text, in further exploring the mechanism of investor sentiment on stock market through deep learning method, which will be beneficial for national regulators and policy departments to develop more reasonable policy guidelines for maintaining the stability of stock market.