Enhancing contextual understanding in NLP: adapting state-of-the-art models for improved sentiment analysis of informal language - Sneha Ravisankar (Nanyang Technological University, 2024)
In the ever-changing landscape of digital communication, social media has given rise to a vast corpus of user-generated content. This content is uniquely characterised by its informal language, including slang, emojis, and ephemeral expressions. Traditional Natural Language Processing (NLP) models often fall short in the task of effectively analysing sentiments in this specific domain. This study reveals that advanced transformer models, notably GPT-3.5 Turbo, RoBERTa, and XLM-R, when fine-tuned on relevant datasets have the potential to surpass traditional models in sentiment analysis classification tasks.