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
Are PPO-ed Language Models Hackable?
Suraj Anand, David Getzen
Context is Important in Depressive Language: A Study of the Interaction Between the Sentiments and Linguistic Markers in Reddit Discussions
Neha Sharma, Kairit Sirts
PRFashion24: A Dataset for Sentiment Analysis of Fashion Products Reviews in Persian
Mehrimah Amirpour, Reza Azmi
Instruction Tuning with Retrieval-based Examples Ranking for Aspect-based Sentiment Analysis
Guangmin Zheng, Jin Wang, Liang-Chih Yu, Xuejie Zhang
Unveiling factors influencing judgment variation in Sentiment Analysis with Natural Language Processing and Statistics
Olga Kellert, Carlos Gómez-Rodríguez, Mahmud Uz Zaman
Large language models for sentiment analysis of newspaper articles during COVID-19: The Guardian
Rohitash Chandra, Baicheng Zhu, Qingying Fang, Eka Shinjikashvili
Aspect-based Sentiment Evaluation of Chess Moves (ASSESS): an NLP-based Method for Evaluating Chess Strategies from Textbooks
Haifa Alrdahi, Riza Batista-Navarro
E2TP: Element to Tuple Prompting Improves Aspect Sentiment Tuple Prediction
Mohammad Ghiasvand Mohammadkhani, Niloofar Ranjbar, Saeedeh Momtazi