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    <titleInfo>
        <title>Identifying Transferable Information Across Domains for Cross-domain Sentiment Classification</title>
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        <namePart type="given">Raksha</namePart>
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        <namePart type="given">Pushpak</namePart>
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    <name type="personal">
        <namePart type="given">Himanshu</namePart>
        <namePart type="given">Sharad</namePart>
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            <namePart type="given">Yusuke</namePart>
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    <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.</abstract>
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        <date>2018-07</date>
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%0 Conference Proceedings
%T Identifying Transferable Information Across Domains for Cross-domain Sentiment Classification
%A Sharma, Raksha
%A Bhattacharyya, Pushpak
%A Dandapat, Sandipan
%A Bhatt, Himanshu Sharad
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F sharma-etal-2018-identifying
%X 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.
%R 10.18653/v1/P18-1089
%U https://aclanthology.org/P18-1089
%U https://doi.org/10.18653/v1/P18-1089
%P 968-978

Markdown (Informal)

[Identifying Transferable Information Across Domains for Cross-domain Sentiment Classification](https://aclanthology.org/P18-1089) (Sharma et al., ACL 2018)

• Identifying Transferable Information Across Domains for Cross-domain Sentiment Classification (Sharma et al., ACL 2018)

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.