2019/03/27 by Yuebing Zhang, Duoqian Miao, Zhang, Yuebing +3
Computer Science · Mathematics · #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Sentiment Analysis and Opinion Mining #Text and Document Classification Technologies #Topic Modeling #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1903.11334
arxiv created 2019/03/27 · openalex publication_date 2019/03/27 · arxiv updated 2019/03/28 · openalex created_date 2019/04/01 · openalex updated_date 2026/07/28
Cross-domain sentiment classification (CDSC) is an importance task in domain adaptation and sentiment classification. Due to the domain discrepancy, a sentiment classifier trained on source domain data may not works well on target domain data. In recent years, many researchers have used deep neural network models for cross-domain sentiment classification task, many of which use Gradient Reversal Layer (GRL) to design an adversarial network structure to train a domain-shared sentiment classifier. Different from those methods, we proposed Hierarchical Attention Generative Adversarial Networks (HAGAN) which alternately trains a generator and a discriminator in order to produce a document representation which is sentiment-distinguishable but domain-indistinguishable. Besides, the HAGAN model applies Bidirectional Gated Recurrent Unit (Bi-GRU) to encode the contextual information of a word and a sentence into the document representation. In addition, the HAGAN model use hierarchical attention mechanism to optimize the document representation and automatically capture the pivots and non-pivots. The experiments on Amazon review dataset show the effectiveness of HAGAN.