Usage
  • 47 views
  • 36 downloads

Polarity Classification of Reviews by Expanding a Preliminary Lexicon

  • Author / Creator
    Yadollahi, Ali
  • 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.

  • Subjects / Keywords
  • Graduation date
    2015-11
  • Type of Item
    Thesis
  • Degree
    Master of Science
  • DOI
    https://doi.org/10.7939/R3GX4507G
  • License
    This thesis is made available by the University of Alberta Libraries with permission of the copyright owner solely for non-commercial purposes. This thesis, or any portion thereof, may not otherwise be copied or reproduced without the written consent of the copyright owner, except to the extent permitted by Canadian copyright law.
  • Language
    English
  • Institution
    University of Alberta
  • Degree level
    Master's
  • Department
    • Department of Computing Science
  • Supervisor / co-supervisor and their department(s)
    • Zaiane, Osmar (Computing Science)
  • Examining committee members and their departments
    • Harms, Janelle (Computing Science)
    • Greiner, Russell (Computing Science)