情感分析是指通过提取文本特征对基于文本的情感倾向进行分类,是自然语言处理领域中的一项重要任务。为了提高文本情感分析的性能,该文将分类任务定义为一个比较问题,并提出了基于比较学习(Comparing to Learn,C2L)的分类模型。C2L的目标是通过与已标注的样本进行比较来对句子进行评分,实际上,通过比较方法进行分类比训练一个过于复杂的模型更加有效。在两个常用数据集上的实验结果表明,C2L的性能优于现有的许多模型;通过调整C2L的内部结构,可以在不同的任务和数据集上取得好的表现。
Abstract
Sentiment analysis refers to the classification of sentiment tendencies in a text. This paper defines the classification task as a comparison problem, and proposes a classification model based on comparative learning (Comparing to Learn, C2L). The goal of C2L is to score sentences by comparing with labeled samples. In fact, classification by comparison is more effective than training an overly complex model. The experimental results of two commonly used datasets show that the performance of C2L is better than many existing models.
关键词
情感分析 /
深度学习 /
比较学习
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Key words
sentiment analysis /
deep learning /
comparing to learn
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参考文献
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脚注
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基金
国家自然科学基金(61976036,61772103);中央高校基本科研业务费项目(DUT22RW110)
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