Text sentiment analysis based on two-channel joint model
Authors: 
Liya Wang, Zhe ChenAuthors Info & Claims
Pages 323 - 326
Abstract
Convolutional neural networks (CNNs) have a strong feature learning capability. Bi-directional long and short-term memory network (BiLSTM) avoids the problem of gradient disappearance in traditional neural networks and is able to learn contextually relevant features better. The attention mechanism plays the role of feature weight distribution, which can better acquire important features. In this paper, we propose a two-channel joint model for sentiment analysis based on IMDB review texts. And a series of comparison experiments are conducted on the dataset, and the accuracy and RMSE values of the method reach 91.52% and 0.2636, respectively. the results show that the proposed model can effectively improve the accuracy of text classification.
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Index Terms
- Text sentiment analysis based on two-channel joint model
Index terms have been assigned to the content through auto-classification.
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Published In
December 2022
406 pages
ISBN:9781450399067
DOI:10.1145/3578741
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 06 March 2023
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- Research-article
- Research
- Refereed limited
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- Science and Technology Innovation Project for Teachers of Zhejiang Institute of Technology and Industry
- Zhejiang Provincial Education Department
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MLNLP 2022
MLNLP 2022: 2022 5th International Conference on Machine Learning and Natural Language Processing
December 23 - 25, 2022
Sanya, China
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