Deep Learning Structure for Cross-Domain Sentiment Classification Based on Improved Cross Entropy and Weight
Joint Authors
Li, Aimin
Xu, Qingzheng
Fei, Rong
Yao, Quanzhu
Zhu, Yuanbo
Wu, Haozheng
Hu, Bo
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-20, 20 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-06-29
Country of Publication
Egypt
No. of Pages
20
Main Subjects
Abstract EN
Within the sentiment classification field, the convolutional neural network (CNN) and long short-term memory (LSTM) are praised for their classification and prediction performance, but their accuracy, loss rate, and time are not ideal.
To this purpose, a deep learning structure combining the improved cross entropy and weight for word is proposed for solving cross-domain sentiment classification, which focuses on achieving better text sentiment classification by optimizing and improving recurrent neural network (RNN) and CNN.
Firstly, we use the idea of hinge loss function (hinge loss) and the triplet loss function (triplet loss) to improve the cross entropy loss.
The improved cross entropy loss function is combined with the CNN model and LSTM network which are tested in the two classification problems.
Then, the LSTM binary-optimize (LSTM-BO) model and CNN binary-optimize (CNN-BO) model are proposed, which are more effective in fitting the predicted errors and preventing overfitting.
Finally, considering the characteristics of the processing text of the recurrent neural network, the influence of input words for the final classification is analysed, which can obtain the importance of each word to the classification results.
The experiment results show that within the same time, the proposed weight-recurrent neural network (W-RNN) model gives higher weight to words with stronger emotional tendency to reduce the loss of emotional information, which improves the accuracy of classification.
American Psychological Association (APA)
Fei, Rong& Yao, Quanzhu& Zhu, Yuanbo& Xu, Qingzheng& Li, Aimin& Wu, Haozheng…[et al.]. 2020. Deep Learning Structure for Cross-Domain Sentiment Classification Based on Improved Cross Entropy and Weight. Scientific Programming،Vol. 2020, no. 2020, pp.1-20.
https://search.emarefa.net/detail/BIM-1209013
Modern Language Association (MLA)
Fei, Rong…[et al.]. Deep Learning Structure for Cross-Domain Sentiment Classification Based on Improved Cross Entropy and Weight. Scientific Programming No. 2020 (2020), pp.1-20.
https://search.emarefa.net/detail/BIM-1209013
American Medical Association (AMA)
Fei, Rong& Yao, Quanzhu& Zhu, Yuanbo& Xu, Qingzheng& Li, Aimin& Wu, Haozheng…[et al.]. Deep Learning Structure for Cross-Domain Sentiment Classification Based on Improved Cross Entropy and Weight. Scientific Programming. 2020. Vol. 2020, no. 2020, pp.1-20.
https://search.emarefa.net/detail/BIM-1209013
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1209013