期刊
IEEE ACCESS
卷 8, 期 -, 页码 37014-37020出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2020.2973711
关键词
Sentiment analysis; neural network; capsule network; BiLSTM
资金
- National Natural Science Foundation of China [61806072]
- Natural Science Foundation of Tianjin City, China [19JCZDJC40000]
Nowadays, 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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据