Automated Topic Model Extraction using Latent Dirichlet Allocation on the Example of Reviews on Car Manufacturers
| Thesis Type | Master |
| Thesis Status |
Finished
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| Student | Franziska Scharpf |
| Final |
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| Start |
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| Thesis Supervisor | |
| Contact |
The aim of this master thesis is to filter the most relevant topics and terms out of a set
of unstructured data, which refer to customer feedback of car manufacturers, and
to present them graphically. For this purpose, the following research question is posed:
How can the most relevant terms and topics be filtered out of documents with the help
of Latent Dirichlet Allocation?