融合左右双边注意力机制的方面级别文本情感分析

方面级别的文本情感分析旨在针对一个句子中具体的方面单词来判断其情感极性.针对方面单词可能由多个单词组成、平均化所有单词的词向量容易导致语义错误或混乱,不同的文本单词对于方面单词的情感极性判断具有不同的影响力的问题,提出一种融合左右的双边注意力机制的方面级别的文本情感分析模型.首先,设计内部注意力机制来处理方面单词,并根据方面单词和上下文单词设计了双边交互注意力机制,最后将双边交互注意力的处理结果与方面单词处理值三个部分级联起来进行分类.模型在SemEval 2014中两个数据集上进行了实验,分别实现了81.33％和74.22％的准确率,相比较于机器学习和结合注意力机制的各种模型取得了更好的效果.

Joint left and right attention mechanisms for aspect-level text sentiment analysis

Aspect words consist of multiple words, averaging the word vectors of all words may lead to semantic errors or confusion, and different context words have different influence on the judgment of emotional polarity for aspect words. This paper proposed a text sentiment analysis model that integrated the left and right bilateral attention mechanisms. First, the model designed an internal attention mechanism to process aspect words, and designed a bilateral interactive attention mechanism based on aspect words and context words, and finally cascaded the three parts of the processing result of bilateral interactive attention and the aspect word processing value for classification. Experiments on the two datasets from SemEval2014 task 4 show that the proposed model achieves 81.33% and 74.22% accuracy respectively, and has achieved better results than models that combine machine learning or attention mechanisms.