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
ChatGPT vs Gemini vs LLaMA on Multilingual Sentiment Analysis
Alessio Buscemi, Daniele Proverbio
A Comparative Analysis of Noise Reduction Methods in Sentiment Analysis on Noisy Bangla Texts
Kazi Toufique Elahi, Tasnuva Binte Rahman, Shakil Shahriar, Samir Sarker, Md. Tanvir Rouf Shawon, G. M. Shahariar
Estimating the severity of dental and oral problems via sentiment classification over clinical reports
Sare Mahdavifar, Seyed Mostafa Fakhrahmad, Elham Ansarifard
Explain Thyself Bully: Sentiment Aided Cyberbullying Detection with Explanation
Krishanu Maity, Prince Jha, Raghav Jain, Sriparna Saha, Pushpak Bhattacharyya
SemEval-2017 Task 4: Sentiment Analysis in Twitter using BERT
Rupak Kumar Das, Dr. Ted Pedersen
Milestones in Bengali Sentiment Analysis leveraging Transformer-models: Fundamentals, Challenges and Future Directions
Saptarshi Sengupta, Shreya Ghosh, Prasenjit Mitra, Tarikul Islam Tamiti
The Effect of Human v/s Synthetic Test Data and Round-tripping on Assessment of Sentiment Analysis Systems for Bias
Kausik Lakkaraju, Aniket Gupta, Biplav Srivastava, Marco Valtorta, Dezhi Wu
Stability Analysis of ChatGPT-based Sentiment Analysis in AI Quality Assurance
Tinghui Ouyang, AprilPyone MaungMaung, Koichi Konishi, Yoshiki Seo, Isao Echizen