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.

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.

Feature Ensemble Plus Sample Selection: Domain Adaptation for Sentiment Classification (Extended Abstract)

AAAI Conferences

The domain adaptation problem arises often in the field of sentiment classification. There are two distinct needs in domain adaptation, namely labeling adaptation and instance adaptation. Most of current research focuses on the former one, while neglects the latter one. In this work, we propose a joint approach, named feature ensemble plus sample selection (SS-FE), which takes both types of adaptation into account. A feature ensemble (FE) model is first proposed to learn a new labeling function in a feature re-weighting manner. Furthermore, a PCA-based sample selection (PCA-SS) method is proposed as an aid to FE for instance adaptation. Experimental results show that the proposed SS-FE approach could gain significant improvements, compared to individual FE and PCA-SS, due to its comprehensive consideration of both labeling adaptation and instance adaptation.

Instance Selection and Instance Weighting for Cross-Domain Sentiment Classification via PU Learning

AAAI Conferences

Due to the explosive growth of the Internet online reviews, we can easily collect a large amount of labeled reviews from different domains. But only some of them are beneficial for training a desired target-domain sentiment classifier. Therefore, it is important for us to identify those samples that are the most relevant to the target domain and use them as training data. To address this problem, a novel approach, based on instance selection and instance weighting via PU learning, is proposed. PU learning is used at first to learn an in-target-domain selector, which assigns an in-target-domain probability to each sample in the training set. For instance selection, the samples with higher in-target-domain probability are used as training data; For instance weighting, the calibrated in-target-domain probabilities are used as sampling weights for training an instance-weighted naive Bayes model, based on the principle of maximum weighted likelihood estimation. The experimental results prove the necessity and effectiveness of the approach, especially when the size of training data is large. It is also proved that the larger the Kullback-Leibler divergence between the training and test data is, the more effective the proposed approach will be.

Incorporating Domain and Sentiment Supervision in Representation Learning for Domain Adaptation

AAAI Conferences

Domain adaptation aims at learning robust classifiers across domains using labeled data from a source domain. Representation learning methods, which project the original features to a new feature space, have been proved to be quite effective for this task. However, these unsupervised methods neglect the domain information of the input and are not specialized for the classification task. In this work, we address two key factors to guide the representation learning process for domain adaptation of sentiment classification — one is domain supervision, enforcing the learned representation to better predict the domain of an input, and the other is sentiment supervision which utilizes the source domain sentiment labels to learn sentiment-favorable representations. Experimental results show that these two factors significantly improve the proposed models as expected.