Sentiment Analysis
Sentiment analysis aims to automatically determine the emotional tone expressed in text, aiming to understand opinions and attitudes. Current research heavily focuses on leveraging large language models (LLMs) like BERT and its variants, along with other architectures such as graph neural networks, to improve accuracy and efficiency, particularly in multimodal settings and low-resource languages. This field is crucial for various applications, including market research, social media monitoring, and understanding public opinion, driving advancements in natural language processing and impacting decision-making across numerous sectors.
Papers
Exploring ChatGPT-based Augmentation Strategies for Contrastive Aspect-based Sentiment Analysis
Lingling Xu, Haoran Xie, S. Joe Qin, Fu Lee Wang, Xiaohui Tao
Hierarchical Narrative Analysis: Unraveling Perceptions of Generative AI
Riona Matsuoka, Hiroki Matsumoto, Takahiro Yoshida, Tomohiro Watanabe, Ryoma Kondo, Ryohei Hisano