From TF-IDF to Transformers: A Comparative and Ensemble Approach to Sentiment Classification
This paper explores sentiment classification using various machine learning models, including traditional methods like Naive Bayes and SVM, alongside transformer-based models such as RoBERTa and DistilBERT. The study evaluated these models on the IMDb dataset for categorizing movie reviews into positive and negative sentiments. RoBERTa achieved the highest accuracy at 93.02%, and an ensemble approach combining multiple models further enhanced classification performance. AI
IMPACT This research highlights RoBERTa's effectiveness in sentiment analysis and demonstrates the benefits of model ensembling for improved accuracy.