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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- Partial requirement for: M.S., Arizona State University, 2016Note typethesis
- Includes bibliographical references (pages 31-32)Note typebibliography
- Field of study: Computer science