关键词: 深度学习, 文本情感分类, 注意力机制, 奇异值分解, 双向门循环单元

Abstract:

The core issue of text emotion classification is how to represent the emotion semantics of the text effectively. However, most current methods only consider the emotion semantics in the text content, ignoring the user and product information related to the text content. Existing methods that incorporate user and product information still exist two problems：（1）The methods can not effectively represent the user and product information. （2）The semantic representation model is too simple and can not effectively represent the context semantic information in the text. To address these issues, this paper puts forward the corresponding solutions：（1） According to the user and the product data, the Singular Value Decomposition（SVD） method is used to obtain the prior information of user and product. This method avoids the training of user and product parameters, which solves the problem that the iterative speed of the model is very slow. （2）Using the bidirectional Gated Recurrent Unit（GRU） model instead of the original simple model, the context semantic information in the text is more effectively combined in the document feature representation. The experimental results show that the proposed method has good classification performances, even achieves the best state-of-the-art result at some experiment datasets. Meanwhile it improves the training speed of the model.

Key words: deep learning, text sentiment classification, Attention, Singular Value Decomposition（SVD）, Bidirectional Gated Recurrent Unit（BGRU）