A Hybrid Neural Network Model for Sentiment Analysis of Financial Texts Using Topic Extraction, Pre-Trained Model, and Enhanced Attention Mechanism Methods

Abstract

In the financial field, texts such as news and commentaries, as carriers of public opinion, have the function of reflecting investor sentiment, influencing investment decisions and market trends, and extracting positive or negative sentiment from them in a timely manner is very important to the investment decisions of fintech companies and investors. However, financial sentiment analysis is challenging due to issues such as unclear sentiment polarity of financial texts, high context-dependency, and highly specialised and specific expressions of the linguistic features of financial texts. In order to overcome these challenges, we design a hybrid topic feature and pre-trained model financial text sentiment analysis model, and we design the method of improved attention mechanism to optimise the model iteratively, the improved attention mechanism reduces the noise caused by the improper allocation of attention to a single word through adaptive attention threshold, attention weight masking mechanism, so as to capture more contextual information and avoid the loss of information, thus improving the stability of the model. This improves the stability of the model. The introduction of thematic features enables the model to capture potential semantic structures and long-distance word associations in the text to enhance the understanding of text context. The experimental results show that the method has an improvement of 2.05%-7.27% in F1 value compared with the baseline method, which is suitable for financial text sentiment analysis.

Keywords

• Financial text analytics
• sentiment analysis
• progressive attention mechanism
• FinBERT