@article{beigman-klebanov-etal-2013-using,
title = "Using Pivot-Based Paraphrasing and Sentiment Profiles to Improve a Subjectivity Lexicon for Essay Data",
author = "Beigman Klebanov, Beata and
Madnani, Nitin and
Burstein, Jill",
editor = "Lin, Dekang and
Collins, Michael",
journal = "Transactions of the Association for Computational Linguistics",
volume = "1",
year = "2013",
address = "Cambridge, MA",
publisher = "MIT Press",
url = "https://aclanthology.org/Q13-1009",
doi = "10.1162/tacl_a_00213",
pages = "99--110",
abstract = "We demonstrate a method of improving a seed sentiment lexicon developed on essay data by using a pivot-based paraphrasing system for lexical expansion coupled with sentiment profile enrichment using crowdsourcing. Profile enrichment alone yields up to 15{\%} improvement in the accuracy of the seed lexicon on 3-way sentence-level sentiment polarity classification of essay data. Using lexical expansion in addition to sentiment profiles provides a further 7{\%} improvement in performance. Additional experiments show that the proposed method is also effective with other subjectivity lexicons and in a different domain of application (product reviews).",
}
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<abstract>We demonstrate a method of improving a seed sentiment lexicon developed on essay data by using a pivot-based paraphrasing system for lexical expansion coupled with sentiment profile enrichment using crowdsourcing. Profile enrichment alone yields up to 15% improvement in the accuracy of the seed lexicon on 3-way sentence-level sentiment polarity classification of essay data. Using lexical expansion in addition to sentiment profiles provides a further 7% improvement in performance. Additional experiments show that the proposed method is also effective with other subjectivity lexicons and in a different domain of application (product reviews).</abstract>
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%0 Journal Article
%T Using Pivot-Based Paraphrasing and Sentiment Profiles to Improve a Subjectivity Lexicon for Essay Data
%A Beigman Klebanov, Beata
%A Madnani, Nitin
%A Burstein, Jill
%J Transactions of the Association for Computational Linguistics
%D 2013
%V 1
%I MIT Press
%C Cambridge, MA
%F beigman-klebanov-etal-2013-using
%X We demonstrate a method of improving a seed sentiment lexicon developed on essay data by using a pivot-based paraphrasing system for lexical expansion coupled with sentiment profile enrichment using crowdsourcing. Profile enrichment alone yields up to 15% improvement in the accuracy of the seed lexicon on 3-way sentence-level sentiment polarity classification of essay data. Using lexical expansion in addition to sentiment profiles provides a further 7% improvement in performance. Additional experiments show that the proposed method is also effective with other subjectivity lexicons and in a different domain of application (product reviews).
%R 10.1162/tacl_a_00213
%U https://aclanthology.org/Q13-1009
%U https://doi.org/10.1162/tacl_a_00213
%P 99-110
Markdown (Informal)
[Using Pivot-Based Paraphrasing and Sentiment Profiles to Improve a Subjectivity Lexicon for Essay Data](https://aclanthology.org/Q13-1009) (Beigman Klebanov et al., TACL 2013)
ACL