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
A BERT based Ensemble Approach for Sentiment Classification of Customer Reviews and its Application to Nudge Marketing in e-Commerce
Sayan Putatunda, Anwesha Bhowmik, Girish Thiruvenkadam, Rahul Ghosh
A Systematic Review of Aspect-based Sentiment Analysis (ABSA): Domains, Methods, and Trends
Yan Cathy Hua, Paul Denny, Katerina Taskova, Jörg Wicker