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Journal of Computers EIMEDLINEScopus

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篇名 Cross-domain Text Sentiment Classification Based on Wasserstein Distance
卷期 30:6
作者 Guoyong Cai 、 Qiang Lin 、 Nannan Chen
頁次 276-285
關鍵字 cross-domain 、 domain adversarial 、 text sentiment analysis 、 wasserstein distance 、 EI 、 MEDLINE 、 Scopus
出刊日期 201912
DOI 10.3966/199115992019123006023

中文摘要

英文摘要

Text sentiment analysis is mainly to detect the sentiment polarity implicit in text data. Most existing supervised learning algorithms are difficult to solve the domain adaptation problem in text sentiment analysis. The key of cross-domain text sentiment analysis is how to extract the domain shared features of different domains in the deep feature space. The proposed method uses denosing autoencoder to extract the deeper shared features with better robustness. In addition, Wasserstein distance-based domain adversarial and orthogonal constraints are combined for better extracting the deep shared features of the different domain. Finally, the deep shared features are used for cross domain sentiment classification. The experimental results on the real data sets show that the proposed method can better adapt to domain differences and achieve higher accuracy.

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