https://scholars.lib.ntu.edu.tw/handle/123456789/413131
標題: | Taxonomy-based regression model for cross-domain sentiment classification | 作者: | Lin C.-K. Lee Y.-Y. Yu C.-H. HSIN-HSI CHEN |
關鍵字: | Domain adaptation;Opinion mining;Sentiment classification | 公開日期: | 2013 | 起(迄)頁: | 1557-1560 | 來源出版物: | International Conference on Information and Knowledge Management | 摘要: | Most cross-domain sentiment classification techniques consider a domain as a whole set of instances for training. However, many online shopping websites organize their data in terms of taxonomy. This paper takes Amazon shopping website as an example, and proposes a tree-structured domain representation scheme in which each node in the tree is encoded as a bit sequence to preserve its relationship with all the other nodes in the tree. To select an appropriate source node for training in the domain taxonomy, we propose a Taxonomy-Based Regression Model (TBRM) which predicts the accuracy loss from multiple source nodes to a target node using the tree-structured domain representation combined with domain similarity and domain complexity. The source node with the smallest accuracy loss is used to train a classifier which makes a prediction on the target node. The results show that our TBRM achieves better performance than the regression models without considering the taxonomy information. Copyright ? 2013 ACM. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/413131 | ISBN: | 9781450322638 | DOI: | 10.1145/2505515.2507843 |
顯示於: | 資訊工程學系 |
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