关键词: 情感分类, 粒度, 双向长短期记忆网络, 注意力交互机制

Abstract:

It is difficult for current deep network models for reviews of products to effectively use user information and product information in reviews. This paper proposes a hierarchical network（HNAIM） model based on the attention interaction mechanism. This model uses hierarchical networks to extract semantic information of different granularities, and uses the attention interaction mechanism in the hierarchical network to capture the important features of users and products to help extract text features. The loss value from the perspective of the user and the loss value from the perspective of the product are used as auxiliary classification information. And the hierarchical network is used to output for the key text features of users or products for training and classification. The comparison results on the three public data sets show that the model is more effective than related models.

Key words: sentiment classification, granularity, Bidirectional Long Short-Term Memory（BiLSTM）, attention interaction mechanism