An Improved Approach for Text Sentiment Classification Based on a Deep Neural Network via a Sentiment Attention Mechanism

Wenkuan Li, Peiyu Liu, Qiuyue Zhang and Wenfeng Liu
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Wenkuan Li: School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
Peiyu Liu: School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
Qiuyue Zhang: School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
Wenfeng Liu: School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China

Future Internet, 2019, vol. 11, issue 4, 1-15

Abstract: Text sentiment analysis is an important but challenging task. Remarkable success has been achieved along with the wide application of deep learning methods, but deep learning methods dealing with text sentiment classification tasks cannot fully exploit sentiment linguistic knowledge, which hinders the development of text sentiment analysis. In this paper, we propose a sentiment-feature-enhanced deep neural network (SDNN) to address the problem by integrating sentiment linguistic knowledge into a deep neural network via a sentiment attention mechanism. Specifically, first we introduce a novel sentiment attention mechanism to help select the crucial sentiment-word-relevant context words by leveraging the sentiment lexicon in an attention mechanism, which bridges the gap between traditional sentiment linguistic knowledge and current popular deep learning methods. Second, we develop an improved deep neural network to extract sequential correlation information and text local features by combining bidirectional gated recurrent units with a convolutional neural network, which further enhances the ability of comprehensive text representation learning. With this design, the SDNN model can generate a powerful semantic representation of text to improve the performance of text sentiment classification tasks. Extensive experiments were conducted to evaluate the effectiveness of the proposed SDNN model on two real-world datasets with a binary-sentiment-label and a multi-sentiment-label. The experimental results demonstrated that the SDNN achieved substantially better performance than the strong competitors for text sentiment classification tasks.

Keywords: deep learning; sentiment attention mechanism; bidirectional gated recurrent unit; convolutional neural network (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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