Context is All You Need: Why Transformers Outperform Traditional ML in Tweet Sentiment
Year: 2025
Type: article
Abstract: This research paper presents a comprehensive comparative analysis of sentiment analysis techniques applied to Twitter data using Natural Language Processing (NLP) methodologies. The study evaluates the performance of conventional machine learning algorithms alongside state-of-the-art transformer-based models on the Sentiment140 dataset containing 1.6 million tweets. We implement and compare four distinct approaches: Naive Bayes and Support Vector Machine (SVM) as traditional machine learning baselines, RoBERTa as an encoder-only transformer, and GPT-2 as decoder-only transformer architecture. Our methodology encompasses extensive preprocessing pipelines, feature extraction techniques, and rigorous evaluation metrics including accuracy, precision, recall, and F1-score. Experimental results demonstrate that transformer-based models significantly outperform conventional approaches, with RoBERTa achieving 93.87% accuracy and GPT-2 attaining 89.01% accuracy, compared to SVM's 86.22% and Naive Bayes' 75.45%. The findings highlight the superior capability of transformer architectures in capturing contextual semantics and handling the complexities inherent in social media text, establishing new benchmarks for Twitter sentiment analysis applications.
Authors: Ayush Kumar, Gajanan M Naik
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7105740301
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