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RoBERTa leads sentiment analysis with 93% accuracy in new study

By PulseAugur Editorial · [2 sources] ·

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.

RANK_REASON The cluster contains an academic paper detailing a comparative study of sentiment classification models.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Dip Biswas Shanto, Mitali Yadav, Prajwal Panth, Suresh Chandra Satapathy ·

    From TF-IDF to Transformers: A Comparative and Ensemble Approach to Sentiment Classification

    arXiv:2605.22003v1 Announce Type: new Abstract: Sentiment analysis, also referred to as opinion mining, primarily tries to extract opinion from any text-based data. In the context of movie reviews and critics, sentimental analysis can be a helpful tool to predict whether a movie …

  2. arXiv cs.CL TIER_1 English(EN) · Suresh Chandra Satapathy ·

    From TF-IDF to Transformers: A Comparative and Ensemble Approach to Sentiment Classification

    Sentiment analysis, also referred to as opinion mining, primarily tries to extract opinion from any text-based data. In the context of movie reviews and critics, sentimental analysis can be a helpful tool to predict whether a movie review is generally positive or negative. It can…

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