li-sentimentanalysismethod-2020.pdf (3.17 MB)
A sentiment analysis method of capsule network based on BiLSTM
journal contribution
posted on 2020-01-01, 00:00 authored by Y Dong, Y Fu, L Wang, Y Chen, Jianxin LiJianxin LiNowadays, capsule network model is widely used in image processing, whose feature engineering is not suitable for sentiment analysis based on texts obviously. In this paper, we propose a capsule network model with BiLSTM named caps-BiLSTM for sentiment analysis to solve the problem, and introduce the experimental results on different datasets. At the beginning of caps-BiLSTM, a convolution layer is used to transform the instance to hide vector. Then the capsule module constructs the capsule representation to the n-gram model. The state probability of a certain capsule is calculated by the capsule model. If the state probability of a given instance is the largest among all capsules, a higher coupling coefficient is assigned. Finally, in order to fusion the data features, the output of the capsule enters into a BiLSTM structure, which is used as a decoder to get the probability representation. Experimental results based on MR, IMDB and SST datasets show that the proposed method can achieve better performances than the traditional machine learning methods and the compared deeping learning models.
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Journal
IEEE AccessVolume
8Pagination
37014 - 37020Publisher
Institute of Electrical and Electronics EngineersLocation
Piscataway, N.J.Publisher DOI
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ISSN
2169-3536eISSN
2169-3536Language
engPublication classification
C1 Refereed article in a scholarly journalUsage metrics
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