Unsupervised Sentiment Analysis by Transferring Multi-source Knowledge

• URL: http://arxiv.org/abs/2105.11902v1
• Date: Sun, 9 May 2021 03:02:19 GMT
• Title: Unsupervised Sentiment Analysis by Transferring Multi-source Knowledge
• Authors: Yong Dai and Jian Liu and Jian Zhang and Hongguang Fu and Zenglin Xu
• Abstract summary: We propose a two-stage domain adaptation framework for sentiment analysis. In the first stage, a multi-task methodology-based shared private architecture is employed to explicitly model the domain common features. In the second stage, two elaborate mechanisms are embedded in the shared private architecture to transfer knowledge from multiple source domains.
• Score: 22.880509132587807
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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