Identifying Transferable Information Across Domains for Cross-domain Sentiment Classification
Raksha Sharma, Pushpak Bhattacharyya, Sandipan Dandapat, Himanshu Sharad Bhatt
Abstract
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, cross-domain sentiment classification is still a challenging 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 χ2 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.- Anthology ID:
- P18-1089
- Volume:
- Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2018
- Address:
- Melbourne, Australia
- Editors:
- Iryna Gurevych, Yusuke Miyao
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 968–978
- Language:
- URL:
- https://aclanthology.org/P18-1089
- DOI:
- 10.18653/v1/P18-1089
- Cite (ACL):
- Raksha Sharma, Pushpak Bhattacharyya, Sandipan Dandapat, and Himanshu Sharad Bhatt. 2018. Identifying Transferable Information Across Domains for Cross-domain Sentiment Classification. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 968–978, Melbourne, Australia. Association for Computational Linguistics.
- Cite (Informal):
- Identifying Transferable Information Across Domains for Cross-domain Sentiment Classification (Sharma et al., ACL 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/P18-1089.pdf