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
Balancing Fairness and Accuracy in Sentiment Detection using Multiple Black Box Models
Abdulaziz A. Almuzaini, Vivek K. Singh
Identifying Chinese Opinion Expressions with Extremely-Noisy Crowdsourcing Annotations
Xin Zhang, Guangwei Xu, Yueheng Sun, Meishan Zhang, Xiaobin Wang, Min Zhang
Neural Contrastive Clustering: Fully Unsupervised Bias Reduction for Sentiment Classification
Jared Mowery