Enhancing Emotion Perception in Sentiment Analysis through Large-Scale Language Models
Qiang Li, Feng Zhao, Jianning Zheng, Hui Xiang, Hong Wei Ouyang, Linlin Zhao, Yongyuan Chen, Ting Biao Guo · 2024
Traditional sentiment analysis methods often overlook the sensitivity to emotional changes in text, which limits their applicability in real-world scenarios. To address this issue, We presents a deep learning based Sentiment-Aware Analysis Model(SAAM), leveraging the powerful representation capability of large-scale language models, namely the sentiment-aware world model introduced herein, in conjunction with the sentiment-sensitive SAAM. Through experimental validation, we demonstrate the effectiveness and performance advantages of the proposed algorithm in sentiment analysis tasks, highlighting the significant role of large-scale language models in sentiment-aware analysis. This approach provides new insights and methods for the field of sentiment analysis.