一种多模特征融合的方面信息情感分类方法

在方面信息情感分类中针对使用循环神经网络编码长距离文本的信息丢失问题,以及使用注意力机制提取情感信息时倾向于关注高频信息偏置问题,提出一种多模特征融合的方面信息情感分类方法,区分单点、多点以及局部三类不同表达模式的情感信息,通过对三类情感信息有侧重的关注、提取与融合,实现各类特征间相互确认与纠错,降低信息丢失与关注偏置问题,达到增强复杂情感表达模式下的方面信息情感分类能力的目的。实验结果表明,使用所提出的方法对三类情感信息进行提取与融合,可以使方面信息情感分类任务在准确率和F1值指标上得到进一步提升。

A Method for Aspect-Level Sentiment Classification Based on Multi-pattern Feature Fusion

Aiming at the problem of information loss in encoding long-distance texts as using recurrent neural networks, and the problem of attention bias to the high-frequency sentiment information when using the attention mechanism to extract sentiment information, this paper proposes a method of aspect-level sentiment classification based on multi-pattern feature fusion. The method divides sentiment information into three categories:single-point sentiment information, multi-point sentiment information and partial sentiment information with different expression patterns, and realizes mutual confirmation and error correction among various types of features by focusing on, extracting and fusing the three types of emotion information. The problems of information loss and attention bias are reduced, and the ability of aspect-level sentiment classification under complex sentiment expression patterns is enhanced. Experimental results show that the accuracy and F1 value of the aspect-level sentiment classification task in extracting and fusing sentiment information can be significantly improved by using the proposed method.