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
Uncovering Political Bias in Emotion Inference Models: Implications for sentiment analysis in social science research
Hubert Plisiecki, Paweł Lenartowicz, Maria Flakus, Artur Pokropek
Dynamic Sentiment Analysis with Local Large Language Models using Majority Voting: A Study on Factors Affecting Restaurant Evaluation
Junichiro Niimi
Personality Analysis for Social Media Users using Arabic language and its Effect on Sentiment Analysis
Mokhaiber Dandash, Masoud Asadpour
New Directions in Text Classification Research: Maximizing The Performance of Sentiment Classification from Limited Data
Surya Agustian, Muhammad Irfan Syah, Nurul Fatiara, Rahmad Abdillah
Fine-Tuning Gemma-7B for Enhanced Sentiment Analysis of Financial News Headlines
Kangtong Mo, Wenyan Liu, Xuanzhen Xu, Chang Yu, Yuelin Zou, Fangqing Xia
Mining United Nations General Assembly Debates
Mateusz Grzyb, Mateusz Krzyziński, Bartłomiej Sobieski, Mikołaj Spytek, Bartosz Pieliński, Daniel Dan, Anna Wróblewska