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
Fine-grained Affective Processing Capabilities Emerging from Large Language Models
Joost Broekens, Bernhard Hilpert, Suzan Verberne, Kim Baraka, Patrick Gebhard, Aske Plaat
UniSA: Unified Generative Framework for Sentiment Analysis
Zaijing Li, Ting-En Lin, Yuchuan Wu, Meng Liu, Fengxiao Tang, Ming Zhao, Yongbin Li
Unimodal Intermediate Training for Multimodal Meme Sentiment Classification
Muzhaffar Hazman, Susan McKeever, Josephine Griffith
Covid-19 Public Sentiment Analysis for Indian Tweets Classification
Mohammad Maksood Akhter, Devpriya Kanojia
Multimodal Multi-loss Fusion Network for Sentiment Analysis
Zehui Wu, Ziwei Gong, Jaywon Koo, Julia Hirschberg