Unsupervised Sentiment Analysis by Transferring Multi-source Knowledge

Abstract

Sentiment analysis (SA) is an important research area in cognitive computation-thus in-depth studies of patterns of sentiment analysis are necessary. At present, rich resource data-based SA has been well developed, while the more challenging and practical multi-source unsupervised SA (i.e. a target domain SA by transferring from multiple source domains) is seldom studied. The challenges behind this problem mainly locate in the lack of supervision information, the semantic gaps among domains (i.e., domain shifts), and the loss of knowledge. However, existing methods either lack the distinguishable capacity of the semantic gaps among domains or lose private knowledge. To alleviate these problems, we propose a two-stage domain adaptation framework. In the first stage, a multi-task methodology-based shared-private architecture is employed to explicitly model the domain common features and the domain-specific features for the labeled source domains. In the second stage, two elaborate mechanisms are embedded in the shared private architecture to transfer knowledge from multiple source domains. The first mechanism is a selective domain adaptation (SDA) method, which transfers knowledge from the closest source domain. And the second mechanism is a target-oriented ensemble (TOE) method, in which knowledge is transferred through a well-designed ensemble method. Extensive experiment evaluations verify that the performance of the proposed framework outperforms unsupervised state-of-the-art competitors. What can be concluded from the experiments is that transferring from very different distributed source domains may degrade the target-domain performance, and it is crucial to choose the proper source domains to transfer from.

arXiv comment17 pages, 4 figures

References in corpus (15)

• Adam: A Method for Stochastic Optimization
• Efficient Estimation of Word Representations in Vector Space
• An Overview of Multi-Task Learning in Deep Neural Networks
• Recurrent Neural Network for Text Classification with Multi-Task Learning
• Domain Separation Networks
• Beyond Sharing Weights for Deep Domain Adaptation
• Low-rank Kernel Learning for Graph-based Clustering
• Adversarial Multi-task Learning for Text Classification
• Deep Multi-Task Learning with Shared Memory
• Multiple Source Domain Adaptation with Adversarial Training of Neural Networks
• Meta Multi-Task Learning for Sequence Modeling
• Multinomial Adversarial Networks for Multi-Domain Text Classification
• Same Representation, Different Attentions: Shareable Sentence Representation Learning from Multiple Tasks
• SHAPED: Shared-Private Encoder-Decoder for Text Style Adaptation
• Unsupervised Domain Adaptation Using Approximate Label Matching