一项最新研究比较了传统机器学习模型与深度学习架构在社交媒体和电子邮件数据上的情感分析性能。在推文情感分类方面，使用TF-IDF特征的逻辑回归模型优于BiLSTM模型，准确率达到73.5%。在电子邮件情感分析方面，支持向量机（SVM）模型表现出卓越的性能，准确率高达98.74%，与LSTM模型相比，在精度和处理速度方面取得了更好的平衡。 AI
影响 表明对于某些文本分类任务，传统机器学习模型可能比复杂的深度学习方法提供更好的性能和效率。
排序理由 该集群包含两篇在arXiv上发表的学术论文，比较了机器学习和深度学习模型在情感分析任务中的应用。
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arXiv:2605.04888v1 Announce Type: new Abstract: The exponential growth of social media has created an urgent need for automated systems to analyze unstructured public sentiment in real time. This study compares a traditional Logistic Regression model using TF-IDF features with a …
The exponential growth of social media has created an urgent need for automated systems to analyze unstructured public sentiment in real time. This study compares a traditional Logistic Regression model using TF-IDF features with a deep learning Bidirectional Long Short-Term Memo…
arXiv:2605.03440v1 Announce Type: new Abstract: The rapid growth of electronic communication has necessitated more robust systems for email classification and sentiment detection. This study presents a comparative performance analysis between traditional machine learning algorith…
The rapid growth of electronic communication has necessitated more robust systems for email classification and sentiment detection. This study presents a comparative performance analysis between traditional machine learning algorithms and deep learning architectures, specifically…