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Neural Computing and Applications
Published in: Neural Computing and Applications 7/2020

04-12-2019 | Deep Learning & Neural Computing for Intelligent Sensing and Control

An emotion classification algorithm based on SPT-CapsNet

Authors: Xian Zhong, Jinhang Liu, Lin Li, Shuqin Chen, Wei Lu, Yuyu Dong, Bingqing Wu, Luo Zhong

Published in: Neural Computing and Applications | Issue 7/2020

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Abstract

Recently, the Capsule Network is an emerging neural network structure that is characterized by the ability to maintain high classification accuracy. By analyzing the difference between Capsule Network and traditional convolutional neural network, it is found that the model compression method applied to the traditional neural network cannot be directly used in the Capsule Network. To address the problem, an IPC-CapsNet compression algorithm is proposed based on the structural characteristics of the Capsule Networks. The algorithm can reduce the computational complexity and compress the scale of model computation on the basis of retaining the accuracy of model classification. Considering the deficiency of Capsule Network processing serialized text data separately, we combined with IPC-CapsNet and then come up with a sentiment classification algorithm SPT-CapsNet. It has conducted a sentiment analysis experiment of MicroBlog dataset. Compared to other methods, our SPT-CapsNet obtained the best performance among the metrics. The SPT-CapsNet improves the running speed and maintains the balance between classification accuracy and computational efficiency.
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Metadata
Title
An emotion classification algorithm based on SPT-CapsNet
Authors
Xian Zhong
Jinhang Liu
Lin Li
Shuqin Chen
Wei Lu
Yuyu Dong
Bingqing Wu
Luo Zhong
Publication date
04-12-2019
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 7/2020
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04621-y

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