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Permanent link (DOI): https://doi.org/10.7939/R3GX4507G

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Polarity Classification of Reviews by Expanding a Preliminary Lexicon Open Access

Descriptions

Other title
Subject/Keyword
hybrid
polarity classification
lexical
Lexicon expansion
machine learning
amazon
imdb
sentiment analysis
Type of item
Thesis
Degree grantor
University of Alberta
Author or creator
Yadollahi, Ali
Supervisor and department
Zaiane, Osmar (Computing Science)
Examining committee member and department
Greiner, Russell (Computing Science)
Harms, Janelle (Computing Science)
Department
Department of Computing Science
Specialization

Date accepted
2015-09-25T15:29:25Z
Graduation date
2015-11
Degree
Master of Science
Degree level
Master's
Abstract
Polarity classification in text is the problem of automatically detecting the general opinion of textual data. Analyzing the general opinion toward a topic of interest is important for different audiences, such as companies, politicians or even regular users. On the other hand, the availability of opinionated text data has been rapidly increasing in WWW during the past decade. However, most of the available text data is not provided with a polarity label and hence one may not be able to learn from it. Addressing this challenge shapes the main motivation of the research in this field of study. Previous works in polarity classification suffer from some drawbacks. As the major drawback, most of the research in the literature naively assumes the availability of the la belled data and takes the advantage of supervising their method by the given ground truth. Furthermore, they do not generally perform well in all domains of textual data, since they are specifically trained over one domain. Although there exists a limited number of works proposing an adaptable classification method, their performance is not promising on the unlabelled data. This thesis tackles the problem of multi-domain unsupervised polarity classification. We propose a hybrid polarity classification technique, which is the combination of a lexical and a learning classification. We also propose a lexicon expansion method, which uses the hybrid classification along with the association rule mining at the sentence level. Our experiments on various datasets show that our hybrid classification method outperforms the previous state-of-the-art unsupervised works. Furthermore, our expansion method is shown to increase the performance of the initial lexicon.
Language
English
DOI
doi:10.7939/R3GX4507G
Rights
Permission is hereby granted to the University of Alberta Libraries to reproduce single copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the thesis and, except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatsoever without the author's prior written permission.
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