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In this thesis multiple approaches are explored to enhance sentiment analysis of tweets. A standard sentiment analysis model with customized features is first trained and tested to establish a baseline.

In this thesis multiple approaches are explored to enhance sentiment analysis of tweets. A standard sentiment analysis model with customized features is first trained and tested to establish a baseline. This is compared to an existing topic based mixture model and a new proposed topic based vector model both of which use Latent Dirichlet Allocation (LDA) for topic modeling. The proposed topic based vector model has higher accuracies in terms of averaged F scores than the other two models.

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    Date Created
    • 2016
    Resource Type
  • Text
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    Note
    • Partial requirement for: M.S., Arizona State University, 2016
      Note type
      thesis
    • Includes bibliographical references (pages 31-32)
      Note type
      bibliography
    • Field of study: Computer science

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    by Swetha Baskaran

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