Co-Train Prompt Knowledge Enhanced Large Language Model with Neural Emotion Topic Model for News Readers' Prediction

Due to the rapid development of the Internet, the abundance of views and emotions surrounding news articles has become significant. Accurate prediction of these emotions plays a crucial role in social media applications, such as sentiment retrieval. In this paper, building upon previous research, we address the challenge of limited emotion tendencies and emotional words in social news by leveraging prompt knowledge and emotional comments posted under news, and develop an end-to-end architecture to predict news readers' emotion. Specifically, our approach introduces a prompt knowledge enhanced large language model as news encoder, which use prompt knowledge to emphasize parts of news which are likely to evoke reader's emotions. For encoding emotional and topic comments, we first propose Neural Emotion Topic Model(NETM), which can generate emotional topics of comments for emotion prediction. Then we use them to develop emotion-topic attention mechanism to capture emotional and topical information while encoding comments. By sharing emotion topic embedding, NETM can be co-trained with attention mechanism and large language models, further enhancing the performance of our architecture. Experiments on four social news datasets show that our proposed end-to-end framework outperforms the state-of-art methods.