Financial Sentiment Analysis Based on Pre-training and TextCNN

Abstract

Since the research of sentiment analysis is mostly concentrated in the field of sentiment analysis on Weibo, and there is less research on sentiment analysis of financial text, this thesis proposes a financial sentiment analysis model based on pre-training and TextCNN. First, the pre-trained model is used to initially extract the emotional features of the text. It can extract text features well, and can extract information between words at arbitrary intervals when processing text sequences. Then use the improved TextCNN to construct a sentiment analysis network to further extract the sentiment features of the text, effectively identify the sentiment of the text, and complete the sentiment analysis of financial text. This thesis conducts experiments on a balanced corpus data set based on financial texts, and compares it with other classic sentiment analysis algorithms. Experimental results show that the proposed method works best in the field of financial text sentiment analysis.