基于三种不同分类算法集成学习的情感文本分析

Wenshuo Bian, Chunzhi Wang, Z. Ye, Lingyu Yan
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引用次数: 8

摘要

为了提高文本情感分析模型的准确率和泛化性能,本文提出了一种集成学习模型,该模型包括逻辑回归、支持向量机和k邻域算法三种不同的分类算法。与单一分类算法相比,该算法具有更好的准确率。实验结果表明,该模型具有良好的泛化性能和鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Emotional Text Analysis Based on Ensemble Learning of Three Different Classification Algorithms
In order to improve the accuracy and generalization performance of text sentiment analysis model, an integrated learning model is proposed in this paper, which includes three different classification algorithms - Logistic regression, support vector machine and K-Neighborhood algorithm. Compared with single classification algorithm, this algorithm shows better accuracy. The experimental results show that the model has good generalization performance and robustness.
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