SC-BiCapsNet: A Sentiment Classification Model Based on Bi-Channel Capsule Network

Most text sentiment classification methods have the shortcomings of insufficient semantic understanding and low classification accuracy. To solve these problems, a sentiment classification model based on capsule network (SC-BiCapsNet) was proposed in this paper. In the stage of word vectorization, t...

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Veröffentlicht in:IEEE access Jg. 7; S. 171801 - 171813
Hauptverfasser: Zhang, Kejun, Jiao, Meng, Chen, Xiaoming, Wang, Zhiqiang, Liu, Biao, Liu, Lei
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Accuracy
Algorithms
capsule network
Classification
Datasets
deep learning
dynamic routing algorithm
Feature extraction
Heuristic algorithms
Machine learning
Representations
Routing
Semantics
Sentiment analysis
Task analysis
Text sentiment classification
Words (language)
ISSN:2169-3536, 2169-3536
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Zusammenfassung:Most text sentiment classification methods have the shortcomings of insufficient semantic understanding and low classification accuracy. To solve these problems, a sentiment classification model based on capsule network (SC-BiCapsNet) was proposed in this paper. In the stage of word vectorization, the G-Word model is presented to improve the ability of text information representation. In the stage of text semantic representation, the COL-Att model is presented to optimize the semantic coding structure and enhance the effect of text semantic expression. In the stage of text classification, the capsule network is devised, and its core dynamic routing algorithm can increase the weight value of important features, thus improving the accuracy of classification results. This paper evaluates the performance of SC-BiCapsNet model on two text datasets, IMDB and NLPCC 2014 dataset. The experimental results demonstrate that our method outperforms the results of other related models on IMDB dataset and gets the highest accuracy of 0.9238. Meanwhile, comparing with the best system in NLPCC2014-Task II, the F1-scsore of SC-BiCapsNet has increased by at least 1.5%, which proves the validity of SC-BiCapsNet. In the future work, we will try to reduce the time consumption and improve the generalization ability of the model.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2953502