2026-07-28T02:09:11Z https://meral.edu.mm/oai

oai:meral.edu.mm:recid/4990 2021-12-13T02:42:19Z 1582963302567:1597824273898 user-ucsy

Automatic Myanmar News Classification Nwet, Khin Thandar Khine, Aye Hnin Soe, Khin Mar Text classification is one of the majortasks of natural language processing andincluded in the interesting research areas oftext data mining, which is about looking forpatterns in natural language text. This paperapplies two well-known classificationalgorithms. Algorithms applied are NaïveBayes and k-Nearest Neighbors (KNN).These well-known algorithms are applied oncollected Myanmar News dataset. Datasetused consists from 1200 documentsbelongs to 4 categories. The goal of textclassification is to classify documents into acertain number of pre-defined categories.News corpus is used for training and testingpurpose of the classifier. Feature selectionalgorithm is used in the proposed system toselect the most relevant features fromtraining data. Results show that precisionand recall values using k-NN is betterthan Naïve Bayes. This research makes acomparative study between mentionedalgorithms. 2017-02-16 http://hdl.handle.net/20.500.12678/0000004990 https://meral.edu.mm/records/4990