STID-Prompt: Prompt learning for sentiment-topic-importance detection in financial news

Abstract

With the development of the Internet and the financial industry, the analysis and judgement of financial news has become increasingly important. Common tasks in this area include sentiment analysis, topic classification, and importance judgement. Existing approaches use fine-tuning paradigms to address these tasks. However, the lack of labeled data in the field of financial news poses a small sample learning problem. The new paradigm represented by prompt learning provides a new way and means to improve the performance of small-sample classification. In addition, existing methods do not focus on research on sentiment, topic, and importance simultaneously, which is also an important factor in evaluating financial news. In practical applications, joint judgment of sentiment, topic, and importance is more effective. Finally, these methods require different tuning for each specific task. They do not consider relationships between individual tasks and cannot utilize information across tasks. This limits their application in complex situations. In this work, we propose a prompt-based financial news classification model (STID-Prompt) to address these issues. For the first two problems, we solve the sentiment analysis task, the topic classification task, the importance judgment task, and the 〈sentiment, topic, importance〉 classification task by designing complex prompt templates. For the third problem, we propose a unique prompt-based joint multi-task learning approach. It learns knowledge from multiple tasks and integrates it into the target task, and the multi-task learning approach further improves the performance of the model. Experimental results show the effectiveness of our approach even with less training data.