A hierarchical neural model for target‐based sentiment analysis. (6th January 2021)

• Record Type: Journal Article
• Authors: Chen, Ke; Ke, Wende
• Is Part Of: Concurrency and computation. Volume 33:Number 10(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-01-06
• Subjects: convolutional neural network -- deep learning -- hierarchical neural model -- long short‐term memory network -- sentiment analysis -- target‐based sentiment analysis; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6184
• Languages: English
• ISSNs: 1532-0626

Abstract

Abstract: A convolutional neural network‐regional long Short‐Term memory (CNN‐RLSTM) is proposed, which is a convolutional neural network‐regional long short‐term memory (CNN‐RLSTM) that combines CNN and regional LSTM. The model can effectively distinguish the affective polarity of different targets through a regional LSTM while reducing the training time of the model. In addition, the model can retain the sentiment information of the whole sentence through a CNN network at the sentence level. Experimental results on different data sets show that the CNN‐RLSTM model is better than the traditional model and the deep network model.