Identifying Transferable Information Across Domains for Cross-domain Sentiment Classification

Raksha Sharma, Pushpak Bhattacharyya, Sandipan Dandapat, Himanshu Sharad Bhatt
2018 Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)  
Getting manually labeled data in each domain is always an expensive and a time consuming task. Cross-domain sentiment analysis has emerged as a demanding concept where a labeled source domain facilitates a sentiment classifier for an unlabeled target domain. However, polarity orientation (positive or negative) and the significance of a word to express an opinion often differ from one domain to another domain. Owing to these differences, crossdomain sentiment classification is still a
more » ... task. In this paper, we propose that words that do not change their polarity and significance represent the transferable (usable) information across domains for cross-domain sentiment classification. We present a novel approach based on χ 2 test and cosine-similarity between context vector of words to identify polarity preserving significant words across domains. Furthermore, we show that a weighted ensemble of the classifiers enhances the cross-domain classification performance.
doi:10.18653/v1/p18-1089 dblp:conf/acl/BhattacharyyaDS18 fatcat:hpwymndgcnagvnjkd4etyikrge