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JOURNALS || EIJO Journal of Engineering, Technology and Innovative Research (EIJO – JETIR) [ ISSN : 2455 - 9172 ]
Sentiment Analysis on Social Media Using Transformer Models

Author Names : 1Ankit Kumar Taneja, 2Parth Sharma, 3Naveen Banjara  Volume 10 Issue 3
Article Overview

This paper explores the application of transformer models for sentiment analysis on social media platforms. Social media presents unique challenges for sentiment classification due to informal language, slang, and sarcastic expressions. Transformer models like BERT, RoBERTa, and DistilBERT, through their contextual understanding and attention mechanisms, outperform traditional methods in capturing sentiment from noisy text. The study outlines the methodology of applying these models, including data preprocessing, model training, and evaluation. It also discusses the challenges posed by bias, computation cost, and ambiguity in social media language. The paper concludes by highlighting future trends, including multilingual sentiment analysis, few-shot learning, and model explainability, positioning transformers as the future of real-time opinion mining.

Keywords: Sentiment Analysis, Social Media, Transformer Models, BERT, RoBERTa, NLP, Emotion Detection, Self-Attention, Fine-Tuning, Sarcasm Detection, Multilingual Analysis

Reference
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