融合特征与注意力的跨领域产品评论情感分析

Sentiment Analysis of Cross-Domain Product Reviews Based on Feature Fusion and Attention Mechanism

【目的】研究针对跨领域情感分类任务中标注数据资源相对匮乏以及从源领域到目标领域情感分类特征重要性区分问题。【方法】提出基于特征融合表示方法与注意力机制的跨领域双向长短时记忆产品评论情感分类模型,融合Bert词向量和跨领域词向量生成跨领域统一特征空间,通过双向长短时记忆网络结合注意力机制提取全局特征和局部特征的重要性权重。【结果】在亚马逊产品公开评论数据集上的对照实验结果表明,该模型跨领域评论情感分类平均准确率达到对照模型的最高值95.93%,比文献中对照模型最高准确率高出9.33%。【局限】需在多领域大规模数据集上进一步检验模型的泛化性,探究源领域知识对目标领域评论情感分类贡献规律。【结论】通过双向长短时记忆网络层学习融合特征能够有效获取情感语义信息,对照实验中对目标领域最有帮助的源领域基本一致。

[Objective] This paper tries to address the issues of labelled data shortage, aiming to distinguish the weights of sentiment characteristics in cross-domain sentiment classification. [Methods] We proposed a sentiment classification model for cross-domain product reviews based on feature fusion representation and the attention mechanism. First, this model integrated Bert and cross-domain word vectors to generate cross-domain unified feature space. Then, it extracted the weights of global and local features through attention mechanism. [Results]We examined our model with public review data from Amazon and found the average accuracy of the proposed model was up-to 95.93%, which was 9.33% higher than the existing model. [Limitations] More research is needed to evaluate our model with large-scale multi-domain data sets. [Conclusions] The proposed model could effectively analyze sentiment information.