Research on Sentiment Analysis of Financial Text Based on Semantic Matching

With the development of the economy, more and more people on social platforms share financial-related information and hope to understand the development of financial markets through relevant financial data. Aiming at this problem, this thesis proposes a sentiment analysis algorithm of financial texts based on semantic matching. First, preliminary text vectorization is performed on financial text data. Then further training and fine-tuning are performed through pre-trained models in order to fully tap the associations between text contexts and better grasp the semantic focus, so as to better model the text content and obtain higher-level financial text semantic representation. Next, through the improved Siamese network semantic matching model, financial text vectors are trained for semantic matching, so that the distance between financial text vectors with the same emotional category is closer and the distance between different classes is farther, which further optimizes the semantic representation of financial text. Finally, support vector machines are used as classifier to perform sentiment classification of financial texts. Experimental results show that compared with other classic sentiment analysis algorithms, the proposed algorithm has the best sentiment analysis effect on financial text.