KORPUS MATNLARINI TEMATIK MODELLASHTIRISHDAGI ZAMONAVIY YONDASHUVLAR
Keywords:
Topic Modeling, Machine Learning, Latent Dirichlet Analysis (LDA), Linguistic Corpus, Topic Classification.Abstract
Natural language processing (NLP) is mainly used in text processing and used to develop applications such as chatbots, automatic editing and analysis, speech recognition, automatic translation, social network monitoring and email filtering. In NLP, topic modeling is a set of algorithms that can be used to automatically summarize large amounts of text. Due to the large number of text features, it becomes difficult to train the models and reduces the performance of the models. Today, topic modeling and dimension reduction in language corpus texts can be performed with various algorithms such as: LDA, Non-negative Matrix Factorization, LSA, PLSA, Lda2Vec, Bert Topic-BERT. This article provides information on the description of topic modeling, areas of application, methods, and approaches, and topics are identified by intellectual processing of language corpus texts through the LDA method and visualized by means of the t-SNE method.
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