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
KoMultiText: Large-Scale Korean Text Dataset for Classifying Biased Speech in Real-World Online Services
Dasol Choi, Jooyoung Song, Eunsun Lee, Jinwoo Seo, Heejune Park, Dongbin Na
Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models
Boyu Zhang, Hongyang Yang, Tianyu Zhou, Ali Babar, Xiao-Yang Liu
"ChatGPT, a Friend or Foe for Education?" Analyzing the User's Perspectives on the Latest AI Chatbot Via Reddit
Forhan Bin Emdad, Benhur Ravuri, Lateef Ayinde, Mohammad Ishtiaque Rahman
MONOVAB : An Annotated Corpus for Bangla Multi-label Emotion Detection
Sumit Kumar Banshal, Sajal Das, Shumaiya Akter Shammi, Narayan Ranjan Chakraborty