Full metadata
Title
Enhanced topic-based modeling for Twitter sentiment analysis
Description
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.
Date Created
2016
Contributors
- Baskaran, Swetha (Author)
- Davulcu, Hasan (Thesis advisor)
- Sen, Arunabha (Committee member)
- Hsiao, Ihan (Committee member)
- Arizona State University (Publisher)
Topical Subject
Resource Type
Extent
vi, 32 pages : color illustrations
Language
Copyright Statement
In Copyright
Primary Member of
Peer-reviewed
No
Open Access
No
Handle
https://hdl.handle.net/2286/R.I.40204
Statement of Responsibility
by Swetha Baskaran
Description Source
Viewed on October 26, 2016
Level of coding
full
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
System Created
- 2016-10-12 02:15:45
System Modified
- 2021-08-30 01:21:50
- 2 years 8 months ago
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