Barbu, Eduard, juhendajaMartens, Ott-KaarelTartu Ülikool. Loodus- ja täppisteaduste valdkondTartu Ülikool. Arvutiteaduse instituut2023-10-302023-10-302020https://hdl.handle.net/10062/93873Topic 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. TheengopenAccessAttribution-NonCommercial-NoDerivatives 4.0 InternationalTopic modelspsychologyLatent Dirichlet Allocationsemantic modelsbakalaureusetöödinformaatikainfotehnoloogiainformaticsinfotechnologyThesis