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. / Dissertation/Thesis / Masters Thesis Computer Science 2016
| Identifer | oai:union.ndltd.org:asu.edu/item:40204 |
| Date | January 2016 |
| Contributors | Baskaran, Swetha (Author), Davulcu, Hasan (Advisor), Sen, Arunabha (Committee member), Hsiao, Ihan (Committee member), Arizona State University (Publisher) |
| Source Sets | Arizona State University |
| Language | English |
| Detected Language | English |
| Type | Masters Thesis |
| Format | 39 pages |
| Rights | http://rightsstatements.org/vocab/InC/1.0/, All Rights Reserved |
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