Mixed network sentiment analysis combining sentiment features and multiple attention

Siqi Zhan, Donghong Qin, Zhizhan Xu
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Abstract

In order to solve the problem of low attention of some emotional words in sentiment analysis tasks and difficulty in capturing long-distance dependence between sentences, this paper proposes a mixed sentiment analysis network (DB-BGA-CNN) that integrates multiple attention mechanisms of sentiment words. First, a more targeted emotional dictionary is obtained by expanding the dictionary, and an emotional word selection segmentation algorithm (DSS) is designed. Secondly, use Bert to encode the word vector of the sentiment words and phrases selected from the sentence and sentiment dictionary respectively to obtain the deep semantic features of the text and perform fusion. Then, use multiple attention mechanisms to realize the enhancement of sentiment analysis capabilities, and discuss which network effect is better; finally, the output vectors of each network are merged, and the activation-pooling layer is used to avoid the occurrence of overfitting. Compared with multiple existing models, the proposed model shows better performance, and the accuracy of the optimal model reaches 95.80%.
结合情感特征和多重关注的混合网络情感分析
为了解决情感分析任务中部分情感词关注度低、句子间长距离依赖难以捕获的问题,本文提出了一种集成多种情感词注意机制的混合情感分析网络(DB-BGA-CNN)。首先,通过扩充词典得到更有针对性的情感词典,并设计了一种情感选词切分算法(DSS)。其次,利用Bert分别对从句子和情感词典中选取的情感词和短语进行词向量编码,获得文本的深层语义特征并进行融合;然后,利用多重注意机制实现情感分析能力的增强,并讨论哪种网络效应更好;最后,对每个网络的输出向量进行合并,并使用激活池层来避免过拟合的发生。与已有的多个模型相比,所提模型表现出更好的性能,最优模型的准确率达到95.80%。
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