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

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.

Inspec keywords: text analysis; regression analysis; learning (artificial intelligence); emotion recognition; image classification; pattern classification

Other keywords: intrinsic semantic dependence information; Coordinated CNN-LSTM-Attention model; emotional expression; text sentiment classification modeling method; CCLA unit; emotional dependence information; state-of-the-art baseline methods; sentiment tendencies; key part; CCLA model

Subjects: Document processing and analysis techniques; Computer vision and image processing techniques; Knowledge engineering techniques; Other topics in statistics

http://iet.metastore.ingenta.com/content/journals/10.1049/cje.2018.11.004
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