Lin C.-K.Lee Y.-Y.Yu C.-H.HSIN-HSI CHEN2019-07-102019-07-1020139781450322638https://scholars.lib.ntu.edu.tw/handle/123456789/413131Most 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.Domain adaptationOpinion miningSentiment classificationconference paper10.1145/2505515.25078432-s2.0-84889571163https://www.scopus.com/inward/record.uri?eid=2-s2.0-84889571163&doi=10.1145%2f2505515.2507843&partnerID=40&md5=d7e89051abd7c655847b993331aa951a