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Future Internet 2018, 10(12), 116;

A Bi-Directional LSTM-CNN Model with Attention for Aspect-Level Text Classification

Shanghai Film Academy, Shanghai University, Shanghai 200444, China
School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China
College of Information Technology, Zhejiang Chinese Medical University, Hangzhou 310053, China
Author to whom correspondence should be addressed.
Received: 25 October 2018 / Revised: 19 November 2018 / Accepted: 22 November 2018 / Published: 24 November 2018
PDF [2307 KB, uploaded 24 November 2018]


The prevalence that people share their opinions on the products and services in their daily lives on the Internet has generated a large quantity of comment data, which contain great business value. As for comment sentences, they often contain several comment aspects and the sentiment on these aspects are different, which makes it meaningless to give an overall sentiment polarity of the sentence. In this paper, we introduce Attention-based Aspect-level Recurrent Convolutional Neural Network (AARCNN) to analyze the remarks at aspect-level. The model integrates attention mechanism and target information analysis, which enables the model to concentrate on the important parts of the sentence and to make full use of the target information. The model uses bidirectional LSTM (Bi-LSTM) to build the memory of the sentence, and then CNN is applied to extracting attention from memory to get the attentive sentence representation. The model uses aspect embedding to analyze the target information of the representation and finally the model outputs the sentiment polarity through a softmax layer. The model was tested on multi-language datasets, and demonstrated that it has better performance than conventional deep learning methods. View Full-Text
Keywords: attention mechanism; NLP; aspect-level sentiment classification attention mechanism; NLP; aspect-level sentiment classification

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Zhu, Y.; Gao, X.; Zhang, W.; Liu, S.; Zhang, Y. A Bi-Directional LSTM-CNN Model with Attention for Aspect-Level Text Classification. Future Internet 2018, 10, 116.

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