Research on the correlation between text emotion mining and stock market based on deep learning

TL;DR

This study employs deep learning, specifically BERT, to analyze financial forum texts and their emotional features, demonstrating their correlation with stock market fluctuations and enhancing prediction accuracy.

Contribution

It introduces a novel application of BERT for financial sentiment analysis and explores its impact on stock market prediction and regulation.

Findings

Emotional features from financial texts reflect stock market fluctuations.

BERT-based sentiment analysis improves prediction accuracy.

Investor sentiment influences stock market dynamics.

Abstract

This paper discusses how to crawl the data of financial forums such as stock bar, and conduct emotional analysis combined with the in-depth learning model. This paper will use the Bert model to train the financial corpus and predict the Shenzhen stock index. Through the comparative study of the maximal information coefficient (MIC), it is found that the emotional characteristics obtained by applying the BERT model to the financial corpus can be reflected in the fluctuation of the stock market, which is conducive to effectively improve the prediction accuracy. At the same time, this paper combines in-depth learning with financial texts to further explore the impact mechanism of investor sentiment on the stock market through in-depth learning, which will help the national regulatory authorities and policy departments to formulate more reasonable policies and guidelines for maintaining the stability of the stock market.