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
A Deep CNN Architecture with Novel Pooling Layer Applied to Two Sudanese Arabic Sentiment Datasets
Mustafa Mhamed, Richard Sutcliffe, Xia Sun, Jun Feng, Eiad Almekhlafi, Ephrem A. Retta
A Simple Information-Based Approach to Unsupervised Domain-Adaptive Aspect-Based Sentiment Analysis
Xiang Chen, Xiaojun Wan
NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis
Shamsuddeen Hassan Muhammad, David Ifeoluwa Adelani, Sebastian Ruder, Ibrahim Said Ahmad, Idris Abdulmumin, Bello Shehu Bello, Monojit Choudhury, Chris Chinenye Emezue, Saheed Salahudeen Abdullahi, Anuoluwapo Aremu, Alipio Jeorge, Pavel Brazdil
Sentiment Analysis: Predicting Yelp Scores
Bhanu Prakash Reddy Guda, Mashrin Srivastava, Deep Karkhanis