Inferring Aspect-Specific Opinion Structure in Product Reviews Using Co-training

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9042)

Abstract

Opinions expressed about a particular subject are often nuanced: a person may have both negative and positive opinions about different aspects of the subject of interest, and these aspect-specific opinions can be independent of the overall opinion. Being able to identify, collect, and count these nuanced opinions in a large set of data offers more insight into the strengths and weaknesses of competing products and services than does aggregating overall ratings. We contribute a new confidence-based co-training algorithm that can identify product aspects and sentiments expressed about such aspects. Our algorithm offers better precision than existing methods, and handles previously unseen language well. We show competitive results on a set of opinionated sentences about laptops and restaurants from a SemEval-2014 Task 4 challenge.

Keywords

Natural Language Processing Opinion Mining Sentiment Analysis Unlabelled Data Parse Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Her Majesty the Queen in Right of Canada 2015

Authors and Affiliations

  1. 1.School of Electrical Engineering and Computer ScienceUniversity of OttawaOttawaCanada
  2. 2.National Research CouncilOttawaCanada

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