An emotion classification algorithm based on SPT-CapsNet

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.

[1]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[2]  Guodong Zhou,et al.  基于双通道LSTM模型的用户性别分类方法研究 (User Gender Classification with Dual-channel LSTM) , 2018, 计算机科学.

[3]  Zhongxia Zhang,et al.  基于Word2Vec的情感词典自动构建与优化 (Automatic Construction and Optimization of Sentiment Lexicon Based on Word2Vec) , 2017, 计算机科学.

[4]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[5]  Christopher D. Manning,et al.  Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks , 2015, ACL.

[6]  Honglak Lee,et al.  Deep Learning for Real-Time Atari Game Play Using Offline Monte-Carlo Tree Search Planning , 2014, NIPS.

[7]  John D. Burger,et al.  Discriminating Gender on Twitter , 2011, EMNLP.

[8]  Yoshua Bengio,et al.  How transferable are features in deep neural networks? , 2014, NIPS.

[9]  Kim-Kwang Raymond Choo,et al.  Adaptive Fusion and Category-Level Dictionary Learning Model for Multiview Human Action Recognition , 2019, IEEE Internet of Things Journal.

[10]  Yiqun Liu,et al.  Microblog Sentiment Analysis with Emoticon Space Model , 2014, Journal of Computer Science and Technology.

[11]  Geoffrey E. Hinton,et al.  Transforming Auto-Encoders , 2011, ICANN.

[12]  Yoshua Bengio,et al.  Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation , 2014, EMNLP.

[13]  D. Opitz,et al.  Popular Ensemble Methods: An Empirical Study , 1999, J. Artif. Intell. Res..

[14]  Ting Liu,et al.  Document Modeling with Gated Recurrent Neural Network for Sentiment Classification , 2015, EMNLP.

[15]  Yoshua Bengio,et al.  Gradient-based learning applied to document recognition , 1998, Proc. IEEE.

[16]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[17]  N. Altman.  An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression , 1992 .

[18]  Geoffrey E. Hinton,et al.  Matrix capsules with EM routing , 2018, ICLR.

[19]  Danushka Bollegala,et al.  Cross-Domain Sentiment Classification Using Sentiment Sensitive Embeddings , 2016, IEEE Transactions on Knowledge and Data Engineering.

[20]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[21]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[22]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[23]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[24]  Meng Wang,et al.  Dictionary learning feature space via sparse representation classification for facial expression recognition , 2017, Artificial Intelligence Review.

[25]  Cheng-Yuan Liou,et al.  Modeling word perception using the Elman network , 2008, Neurocomputing.

[26]  Raymond Y. K. Lau,et al.  Bootstrapping Social Emotion Classification with Semantically Rich Hybrid Neural Networks , 2017, IEEE Transactions on Affective Computing.

[27]  H. Robbins.  A Stochastic Approximation Method , 1951 .

[28]  Yoshua Bengio,et al.  Generative Adversarial Nets , 2014, NIPS.

[29]  Cícero Nogueira dos Santos,et al.  Deep Convolutional Neural Networks for Sentiment Analysis of Short Texts , 2014, COLING.

[30]  Ruihua Qi.  Identifying Chinese Microblog Author Gender Based on Dependency , 2017 .