English(EN) From TF-IDF to Transformers: A Comparative and Ensemble Approach to Sentiment Classification

RoBERTa 在一项新研究中以 93% 的准确率引领情感分析

作者 PulseAugur 编辑部 · [2 个来源] · 2026-05-21 05:00

本文探讨了使用各种机器学习模型进行情感分类，包括朴素贝叶斯和 SVM 等传统方法，以及 RoBERTa 和 DistilBERT 等基于 Transformer 的模型。该研究在 IMDb 数据集上评估了这些模型，用于将电影评论分类为正面和负面情感。RoBERTa 的准确率最高，达到 93.02%，而结合多个模型的集成方法进一步提高了分类性能。 AI

影响这项研究突出了 RoBERTa 在情感分析中的有效性，并展示了模型集成在提高准确性方面的优势。

排序理由该集群包含一篇详细介绍情感分类模型比较研究的学术论文。

在 arXiv cs.CL 阅读 →

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报道来源 [2]

arXiv cs.CL TIER_1 English(EN) · Dip Biswas Shanto, Mitali Yadav, Prajwal Panth, Suresh Chandra Satapathy · 2026-05-22 04:00

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 …
arXiv cs.CL TIER_1 English(EN) · Suresh Chandra Satapathy · 2026-05-21 05:00

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…

报道来源 [2]

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

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

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