对比多种模型，发现RoBERTa在影评情感分类中表现最佳。
From TF-IDF to Transformers: A Comparative and Ensemble Approach to Sentiment Classification
- 用TF-IDF到Transformer的多种模型做情感分类
- RoBERTa准确率达93.02%，优于其他模型
- 集成所有模型提升性能，适合实际应用
情感分析旨在从文本数据中提取观点。在影评场景中，可帮助判断评论整体为正面或负面。传统机器学习模型依赖统计词表示，难以准确理解语境或隐含情感。本文基于IMDb数据集，评估了朴素贝叶斯、逻辑回归、支持向量机（SVM）、LightGBM、LSTM，以及基于Transformer的RoBERTa和DistilBERT等模型。通过准确率、精确率、召回率、F1分数和ROC-AUC进行对比，结果显示RoBERTa表现最优，准确率达到93.02%。此外，融合所有模型的软投票集成方法进一步提升了分类效果，表明模型集成在情感分析中具有有效性。
原文摘要 · Abstract (English)
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 be difficult for the ML models to understand the context or metaphysical sentiment accurately, as ML models rely largely on statistical word representations. The objective of this paper is to examine and categorise movie reviews into positive and negative sentiments. Diverse machine learning models are considered in doing so, and Natural Language Processing (NLP) methodologies are employed for data preprocessing and model assessment. The IMDb dataset is used. Specifically, Naive Bayes, Logistic Regression, Support Vector Machines (SVM), LightGBM, LSTM, and transformer-based models such as RoBERTa and DistilBERT were evaluated. After a lot of testing with accuracy, precision, recall, F1-score, and ROC-AUC, RoBERTa performed better than all the other models, with an accuracy of 93.02%. A soft voting ensemble that combined all the models also improved classification performance, showing that model ensembling works well for sentiment analysis.
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