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RoBERTa leads sentiment analysis with 93% accuracy in new study

作者 PulseAugur 编辑部 · [2 个来源] ·

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

影响 This research highlights RoBERTa's effectiveness in sentiment analysis and demonstrates the benefits of model ensembling for improved accuracy.

排序理由 The cluster contains an academic paper detailing a comparative study of sentiment classification models.

在 arXiv cs.CL 阅读 →

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报道来源 [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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