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
Towards Robust Aspect-based Sentiment Analysis through Non-counterfactual Augmentations
Xinyu Liu, Yan Ding, Kaikai An, Chunyang Xiao, Pranava Madhyastha, Tong Xiao, Jingbo Zhu
L3Cube-MahaSent-MD: A Multi-domain Marathi Sentiment Analysis Dataset and Transformer Models
Aabha Pingle, Aditya Vyawahare, Isha Joshi, Rahul Tangsali, Raviraj Joshi
Public Attitudes Toward ChatGPT on Twitter: Sentiments, Topics, and Occupations
Ratanond Koonchanok, Yanling Pan, Hyeju Jang
Constructing Colloquial Dataset for Persian Sentiment Analysis of Social Microblogs
Mojtaba Mazoochi, Leila Rabiei, Farzaneh Rahmani, Zeinab Rajabi
Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models
Boyu Zhang, Hongyang Yang, Xiao-Yang Liu
Adversarial Capsule Networks for Romanian Satire Detection and Sentiment Analysis
Sebastian-Vasile Echim, Răzvan-Alexandru Smădu, Andrei-Marius Avram, Dumitru-Clementin Cercel, Florin Pop
Detect Depression from Social Networks with Sentiment Knowledge Sharing
Yan Shi, Yao Tian, Chengwei Tong, Chunyan Zhu, Qianqian Li, Mengzhu Zhang, Wei Zhao, Yong Liao, Pengyuan Zhou