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Enhanced Topic-Based Modeling for Twitter Sentiment Analysis


Abstract 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.
Created Date 2016
Contributor Baskaran, Swetha (Author) / Davulcu, Hasan (Advisor) / Sen, Arunabha (Committee member) / Hsiao, Ihan (Committee member) / Arizona State University (Publisher)
Subject Computer science / Topic-based Sentiment Analysis
Type Masters Thesis
Extent 39 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Masters Thesis Computer Science 2016
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis