Taxonomy-based regression model for cross-domain sentiment classification

Authors:
Cong-Kai Lin;Yang-Yin Lee;Chi-Hsin Yu;Hsin-Hsi Chen
Affiliations:
National Taiwan University, Taipei, Taiwan Roc;National Taiwan University, Taipei, Taiwan Roc;National Taiwan University, Taipei, Taiwan Roc;National Taiwan University, Taipei, Taiwan Roc
Venue:
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Year:
2013

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Abstract

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.