Review Sentiment Scoring via a Parse-and-Paraphrase Paradigm

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Review Sentiment Scoring via a Parse-and-Paraphrase Paradigm
This paper presents a parse-and-paraphrase paradigm to assess the degrees of sentiment for product reviews. Sentiment identification has been well studied; however, most previous work provides binary polarities only (positive and negative), and the polarity of sentiment is simply reversed when a negation is detected. The extraction of lexical features such as unigram/bigram also complicates the sentiment classification task, as linguistic structure such as implicit long-distance dependency is often disregarded. In this paper, we propose an approach to extracting adverb-adjective-noun phrases based on clause structure obtained by parsing sentences into a hierarchical representation. We also propose a robust general solution for modeling the contribution of adverbials and negation to the score for degree of sentiment. In an application involving extracting aspect-based pros and cons from restaurant reviews, we obtained a 45% relative improvement in recall through the use of parsing meth...
Jingjing Liu, Stephanie Seneff
Added 17 Feb 2011
Updated 17 Feb 2011
Type Journal
Year 2009
Authors Jingjing Liu, Stephanie Seneff
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