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

  • Polarity classification
  • Imdb
  • Lexical
  • Amazon
  • Machine learning
  • Sentiment analysis
  • Hybrid
  • Lexicon expansion

• Graduation date

  Fall 2015

• 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.