A Text Sentiment Classification Modeling Method Based on Coordinated CNN-LSTM-Attention Model  

作　　者：ZHANG Yangsen ZHENG Jia JIANG Yuru HUANG Gaijuan CHEN Ruoyu 

机构地区：[1]Institute of Intelligent Information Processing,Beijing Information Science and Technology University [2]Beijing Laboratory of National Economic Security Early-Warning Engineering

出　　处：《Chinese Journal of Electronics》2019年第1期120-126,共7页电子学报（英文版）

基　　金：supported by the National Natural Science Foundation of China(No.61772081,No.61602044);the Science and Technology Development Project of Beijing Municipal Education Commission(No.KM201711232014)

摘　　要：The major challenge that text sentiment classification modeling faces is how to capture the intrinsic semantic, emotional dependence information and the key part of the emotional expression of text. To solve this problem, we proposed a Coordinated CNNLSTM-Attention(CCLA) model. We learned the vector representations of sentence with CCLA unit. Semantic and emotional information of sentences and their relations are adaptively encoded to vector representations of document.We used softmax regression classifier to identify the sentiment tendencies in the text. Compared with other methods, the CCLA model can well capture the local and long distance semantic and emotional information.Experimental results demonstrated the effectiveness of CCLA model. It shows superior performances over several state-of-the-art baseline methods.The major challenge that text sentiment classification modeling faces is how to capture the intrinsic semantic, emotional dependence information and the key part of the emotional expression of text. To solve this problem, we proposed a Coordinated CNNLSTM-Attention(CCLA) model. We learned the vector representations of sentence with CCLA unit. Semantic and emotional information of sentences and their relations are adaptively encoded to vector representations of document.We used softmax regression classifier to identify the sentiment tendencies in the text. Compared with other methods, the CCLA model can well capture the local and long distance semantic and emotional information.Experimental results demonstrated the effectiveness of CCLA model. It shows superior performances over several state-of-the-art baseline methods.

关 键 词：COORDINATED CNN-LSTM-Attention SENTIMENT analysis Text modeling SEMANTIC information 

分 类 号：TN[电子电信]