Inferring aspect-specific opinion structure in product reviews using co-training

From National Research Council Canada

DOIResolve DOI: https://doi.org/10.1007/978-3-319-18117-2_17
AuthorSearch for: 1 ; Search for:
Name affiliation
  1. National Research Council Canada. Information and Communication Technologies
FormatText
TypeBook chapter
Proceedings titleComputational Linguistics and Intelligent Text Processing : 16th International Conference, CICLing 2015, Cairo, Egypt, April 14-20, 2015, Proceedings, Part II
Series titleLecture Notes In Computer Science; Volume 9042
Conference16th International Conference on Intelligent Text Processing and Computational Linguistics (CICLing 2015), April 14-20, 2015, Cairo, Egypt
ISSN0302-9743
ISBN978-3-319-18116-5
978-3-319-18117-2
Volume9042
Pages225–240; # of pages: 16
SubjectLinguistics; Text processing; Co-training; Co-training algorithm; Product reviews; Computational linguistics
Abstract
Opinions expressed about a particular subject are often nuanced:a person may have both negative and positive opinions aboutdifferent aspects of the subject of interest, and these aspect-specific opinionscan be independent of the overall opinion. Being able to identify,collect, and count these nuanced opinions in a large set of data offersmore insight into the strengths and weaknesses of competing productsand services than does aggregating overall ratings. We contribute a newconfidence-based co-training algorithm that can identify product aspectsand sentiments expressed about such aspects. Our algorithm offers betterprecision than existing methods, and handles previously unseen languagewell. We show competitive results on a set of opinionated sentences aboutlaptops and restaurants from a SemEval-2014 Task 4 challenge.
Publication date
PublisherSpringer International Publishing
LanguageEnglish
Peer reviewedYes
NPARC number21276947
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Record identifier10f88688-ed84-4c7f-a02a-1fdc72d0fa02
Record created2015-11-10
Record modified2019-01-31
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