Evolution of Topics in the Psychology Domain
| dc.contributor.advisor | Barbu, Eduard, juhendaja | |
| dc.contributor.author | Martens, Ott-Kaarel | |
| dc.contributor.other | Tartu Ülikool. Loodus- ja täppisteaduste valdkond | et |
| dc.contributor.other | Tartu Ülikool. Arvutiteaduse instituut | et |
| dc.date.accessioned | 2023-10-30T14:15:50Z | |
| dc.date.available | 2023-10-30T14:15:50Z | |
| dc.date.issued | 2020 | |
| dc.description.abstract | Topic modeling is a set of statistical methods for modeling collections of discrete data such as text corpora. It is used as a text-mining tool to discover the hidden semantic structures in a text body. Latent Dirichlet Allocation, a particular method for topic modeling is a generative probabilistic model that models texts as a mixture of underlying topics. In this thesis, Latent Dirichlet Allocation is used on a large corpus of texts from the domain of psychology. A model with 100 topics is generated, and the resulting topics are labeled. The occurrence of the topics is analysed over a time span of 40 years. The | et |
| dc.identifier.uri | https://hdl.handle.net/10062/93873 | |
| dc.language.iso | eng | et |
| dc.publisher | Tartu Ülikool | et |
| dc.rights | openAccess | et |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.subject | Topic models | et |
| dc.subject | psychology | et |
| dc.subject | Latent Dirichlet Allocation | et |
| dc.subject | semantic models | et |
| dc.subject.other | bakalaureusetööd | et |
| dc.subject.other | informaatika | et |
| dc.subject.other | infotehnoloogia | et |
| dc.subject.other | informatics | et |
| dc.subject.other | infotechnology | et |
| dc.title | Evolution of Topics in the Psychology Domain | et |
| dc.type | Thesis | et |