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
Extracting Structured Insights from Financial News: An Augmented LLM Driven Approach
Rian Dolphin, Joe Dursun, Jonathan Chow, Jarrett Blankenship, Katie Adams, Quinton Pike
Link Polarity Prediction from Sparse and Noisy Labels via Multiscale Social Balance
Marco Minici, Federico Cinus, Francesco Bonchi, Giuseppe Manco
ZZU-NLP at SIGHAN-2024 dimABSA Task: Aspect-Based Sentiment Analysis with Coarse-to-Fine In-context Learning
Senbin Zhu, Hanjie Zhao, Xingren Wang, Shanhong Liu, Yuxiang Jia, Hongying Zan
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