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

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

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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 CNN-LSTM-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.

Coordinated CNN-LSTM-AttentionSentiment analysisText modelingSemantic information

ZHANG Yangsen、ZHENG Jia、JIANG Yuru、HUANG Gaijuan、CHEN Ruoyu

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Institute of Intelligent Information Processing, Beijing Information Science and Technology University, Beijing 100192, China

Beijing Laboratory of National Economic Security Early-Warning Engineering,Beijing 100192,China

This work is supported by the National Natural Science Foundation of ChinaThis work is supported by the National Natural Science Foundation of ChinaScience and Technology Development Project of Beijing Municipal Education Commission

6177208161602044KM201711232014

2019

10.1049/cje.2018.11.004

中国电子杂志(英文版)

中国电子杂志(英文版)

CSTPCDCSCDSCIEI
ISSN：1022-4653
年,卷(期)：2019.28(1)
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