Sentiments Analysis of Reviews Based on ARCNN Model

2017 International Conference on Artificial Intelligence Applications and Technologies

计算机科学

Xu, Xiaoyu^1 ; Xu, Ming^1 ; Xu, Jian^1 ; Zheng, Ning^1 ; Yang, Tao^2

School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China^1

Key Lab of the Third Research Institute, Ministry of Public Security, China^2

关键词: Attention mechanisms;    Feature vectors;    Product reviews;    Semantic relations;    Sentiment classification;    Softmax classifiers;    Time series informations;    Vector representations;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/261/1/012023/pdf
DOI  :  10.1088/1757-899X/261/1/012023

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

The sentiments analysis of product reviews is designed to help customers understand the status of the product. The traditional method of sentiments analysis relies on the input of a fixed feature vector which is performance bottleneck of the basic codec architecture. In this paper, we propose an attention mechanism with BRNN-CNN model, referring to as ARCNN model. In order to have a good analysis of the semantic relations between words and solves the problem of dimension disaster, we use the GloVe algorithm to train the vector representations for words. Then, ARCNN model is proposed to deal with the problem of deep features training. Specifically, BRNN model is proposed to investigate non-fixed-length vectors and keep time series information perfectly and CNN can study more connection of deep semantic links. Moreover, the attention mechanism can automatically learn from the data and optimize the allocation of weights. Finally, a softmax classifier is designed to complete the sentiment classification of reviews. Experiments show that the proposed method can improve the accuracy of sentiment classification compared with benchmark methods.