This master’s thesis deals with using Text mining as a method to predict tags of articles. It describes the iterative way of handling big data files, parsing the data, cleaning the data and scoring of terms in article using TF-IDF. It describes in detail the flow of program written in programming language Python 3.4.3. The result of processing more than 1 million articles from Wikipedia database is a dictionary of English terms. By using this dictionary one is capable of determining the most important terms from article in corpus of articles. Relevancy of consequent tags proves the method used in this case.
| Identifer | oai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:224862 |
| Date | January 2015 |
| Creators | Harár, Pavol |
| Contributors | Galáž, Zoltán, Kříž, Jiří |
| Publisher | Vysoké učení technické v Brně. Fakulta podnikatelská |
| Source Sets | Czech ETDs |
| Language | Slovak |
| Detected Language | English |
| Type | info:eu-repo/semantics/masterThesis |
| Rights | info:eu-repo/semantics/restrictedAccess |
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