Hierarchical Attention Generative Adversarial Networks for Cross-domain Sentiment Classification

arXiv.org Machine Learning

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.


Li

AAAI Conferences

Cross-domain sentiment classification aims to leverage useful information in a source domain to help do sentiment classification in a target domain that has no or little supervised information. Existing cross-domain sentiment classification methods cannot automatically capture non-pivots, i.e., the domain-specific sentiment words, and pivots, i.e., the domain-shared sentiment words, simultaneously. In order to solve this problem, we propose a Hierarchical Attention Transfer Network (HATN) for cross-domain sentiment classification. The proposed HATN provides a hierarchical attention transfer mechanism which can transfer attentions for emotions across domains by automatically capturing pivots and non-pivots. Besides, the hierarchy of the attention mechanism mirrors the hierarchical structure of documents, which can help locate the pivots and non-pivots better. The proposed HATN consists of two hierarchical attention networks, with one named P-net aiming to find the pivots and the other named NP-net aligning the non-pivots by using the pivots as a bridge. Specifically, P-net firstly conducts individual attention learning to provide positive and negative pivots for NP-net. Then, P-net and NP-net conduct joint attention learning such that the HATN can simultaneously capture pivots and non-pivots and realize transferring attentions for emotions across domains. Experiments on the Amazon review dataset demonstrate the effectiveness of HATN.


Li

AAAI Conferences

In the literature, various approaches have been proposedto address the domain adaptation problem in sentiment classification (also called cross-domainsentiment classification). However, the adaptation performance normally much suffers when the data distributionsin the source and target domains differ significantly. In this paper, we suggest to perform activelearning for cross-domain sentiment classification by actively selecting a smallamount of labeled data in the target domain. Accordingly, we propose an novel activelearning approach for cross-domain sentiment classification.


Linking Heterogeneous Input Features with Pivots for Domain Adaptation

AAAI Conferences

Sentiment classification aims to automatically predict sentiment polarity (e.g., positive or negative) of user generated sentiment data (e.g., reviews, blogs). In real applications, these user generated sentiment data can span so many different domains that it is difficult to manually label training data for all of them. Hence, this paper studies the problem of domain adaptation for sentiment classification where a systemtrained using labeled reviews from a source domain is deployed to classify sentimentsof reviews in a different target domain. In this paper, we propose to link heterogeneous input features with pivots via joint non-negative matrix factorization. This is achieved by learning the domain-specific information from different domains into unified topics, with the help of pivots across all domains. We conduct experiments on a benchmark composed of reviews of 4 types of Amazon products. Experimental results show that our proposed approach significantly outperforms the baseline method, and achieves an accuracy which is competitive with the state-of-the-art methods for sentiment classification adaptation.


Structural Correspondence Learning for Cross-lingual Sentiment Classification with One-to-many Mappings

arXiv.org Machine Learning

Structural correspondence learning (SCL) is an effective method for cross-lingual sentiment classification. This approach uses unlabeled documents along with a word translation oracle to automatically induce task specific, cross-lingual correspondences. It transfers knowledge through identifying important features, i.e., pivot features. For simplicity, however, it assumes that the word translation oracle maps each pivot feature in source language to exactly only one word in target language. This one-to-one mapping between words in different languages is too strict. Also the context is not considered at all. In this paper, we propose a cross-lingual SCL based on distributed representation of words; it can learn meaningful one-to-many mappings for pivot words using large amounts of monolingual data and a small dictionary. We conduct experiments on NLP\&CC 2013 cross-lingual sentiment analysis dataset, employing English as source language, and Chinese as target language. Our method does not rely on the parallel corpora and the experimental results show that our approach is more competitive than the state-of-the-art methods in cross-lingual sentiment classification.