Using Pivot-Based Paraphrasing and Sentiment Profiles to Improve a Subjectivity Lexicon for Essay Data | Zendy

Beata Beigman Klebanov | Zendy; Nitin Madnani | Zendy; Jill Burstein | Zendy

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Using Pivot-Based Paraphrasing and Sentiment Profiles to Improve a Subjectivity Lexicon for Essay Data

Author(s) -

Beata Beigman Klebanov,

Nitin Madnani,

Jill Burstein

Publication year - 2013

Publication title -

transactions of the association for computational linguistics

Language(s) - English

Resource type - Journals

ISSN - 2307-387X

DOI - 10.1162/tacl_a_00213

Subject(s) - lexicon , computer science , subjectivity , natural language processing , artificial intelligence , sentiment analysis , crowdsourcing , sentence , polarity (international relations) , world wide web , philosophy , genetics , epistemology , biology , cell

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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