https://scholars.lib.ntu.edu.tw/handle/123456789/413122
標題: | Exploring ensemble of models in taxonomy-based cross-domain sentiment classification | 作者: | Lin C.-K. Lee Y.-Y. Yu C.-H. HSIN-HSI CHEN |
關鍵字: | Domain adaptation;Ensemble learning;Opinion mining;Sentiment classification | 公開日期: | 2014 | 起(迄)頁: | 1279-1288 | 來源出版物: | 2014 ACM International Conference on Information and Knowledge Management | 摘要: | Most cross-domain sentiment classification techniques consider a domain as a whole set of opinionated instances for training. However, many online shopping websites organize their data in terms of taxonomy. With multiple domains (or, nodes) organized in a tree-structured representation, we propose a general ensemble algorithm which takes into account: 1) the model application, 2) the model weight and 3) the strategies for selecting the most related models with respect to a target node. The traditional sentiment classification technique SVM and the transfer learning algorithm Spectral Features Alignment (SFA) were applied as our model applications. In addition, the model weight takes the tree information and the similarity between domains into account. Finally, two strategies, cosine function and taxonomy-based regression model (TBRM) are proposed to select the most related models with respect to a target node. Experimental results showed both (cosine function and TBRM) proposed strategies outperform two baselines on an Amazon dataset. Three tasks of the proposed methods surpass the gold standard generated by the in-domain classifiers trained on the labeled data from the target nodes. Good results from the three tasks enable this algorithm to shed some new light on eliminating the major difficulties in transfer learning research: the distribution gap. Copyright ? 2014 ACM. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/413122 | ISBN: | 9781450325981 | DOI: | 10.1145/2661829.2662071 |
顯示於: | 資訊工程學系 |
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