Sentiment Analysis in Social Media Using Transformers

Abstract

Sentiment analysis, a crucial component of natural language processing, has gained significant attention due to its relevance in understanding and harnessing user opinions expressed on social media platforms. In this research, we explore the application of advanced machine learning models, specifically Transformers, for sentiment analysis in the context of social media data. Transformers have shown remarkable success in various NLP tasks, making them a promising choice for this domain.

This study focuses on the development and evaluation of sentiment analysis models that leverage the power of Transformers to capture contextual information and nuances in social media text. We present a comprehensive analysis of sentiment classification, encompassing both binary and multiclass sentiment labels, with a specific emphasis on handling user-generated content that is often informal, noisy, and context-dependent.

The experiments conducted in this research demonstrate the efficacy of Transformer-based models in achieving state-of-the-art performance in sentiment analysis tasks. We evaluate and compare various pre-trained Transformer models, fine-tuning strategies, and data preprocessing techniques to provide insights into the optimal approach for sentiment analysis on social media data.