{"created":"2020-09-01T15:36:50.015957+00:00","id":4990,"links":{},"metadata":{"_buckets":{"deposit":"926cc459-69cd-4c56-8ee6-410c3c3bed64"},"_deposit":{"id":"4990","owners":[],"pid":{"revision_id":0,"type":"recid","value":"4990"},"status":"published"},"_oai":{"id":"oai:meral.edu.mm:recid/4990","sets":["1582963302567:1597824273898"]},"communities":["ucsy"],"item_1583103067471":{"attribute_name":"Title","attribute_value_mlt":[{"subitem_1551255647225":"Automatic Myanmar News Classification","subitem_1551255648112":"en"}]},"item_1583103085720":{"attribute_name":"Description","attribute_value_mlt":[{"interim":"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."}]},"item_1583103108160":{"attribute_name":"Keywords","attribute_value_mlt":[{"interim":"text classification"},{"interim":"Natural Language Processing"},{"interim":"Naive Bayes"},{"interim":"k-Nearest Neighbors classifier"}]},"item_1583103120197":{"attribute_name":"Files","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","date":[{"dateType":"Available","dateValue":"2019-07-15"}],"displaytype":"preview","filename":"proceeding_total-pages-401-408.pdf","filesize":[{"value":"3328 Kb"}],"format":"application/pdf","licensetype":"license_note","mimetype":"application/pdf","url":{"url":"https://meral.edu.mm/record/4990/files/proceeding_total-pages-401-408.pdf"},"version_id":"0aacb34c-28f3-4117-a74b-93200cc3cf47"}]},"item_1583103131163":{"attribute_name":"Journal articles","attribute_value_mlt":[{"subitem_issue":"","subitem_journal_title":"Fifteenth International Conference on Computer Applications(ICCA 2017)","subitem_pages":"","subitem_volume":""}]},"item_1583103147082":{"attribute_name":"Conference papers","attribute_value_mlt":[{"subitem_acronym":"","subitem_c_date":"","subitem_conference_title":"","subitem_part":"","subitem_place":"","subitem_session":"","subitem_website":""}]},"item_1583103211336":{"attribute_name":"Books/reports/chapters","attribute_value_mlt":[{"subitem_book_title":"","subitem_isbn":"","subitem_pages":"","subitem_place":"","subitem_publisher":""}]},"item_1583103233624":{"attribute_name":"Thesis/dissertations","attribute_value_mlt":[{"subitem_awarding_university":"","subitem_supervisor(s)":[{"subitem_supervisor":""}]}]},"item_1583105942107":{"attribute_name":"Authors","attribute_value_mlt":[{"subitem_authors":[{"subitem_authors_fullname":"Nwet, Khin Thandar"},{"subitem_authors_fullname":"Khine, Aye Hnin"},{"subitem_authors_fullname":"Soe, Khin Mar"}]}]},"item_1583108359239":{"attribute_name":"Upload type","attribute_value_mlt":[{"interim":"Publication"}]},"item_1583108428133":{"attribute_name":"Publication type","attribute_value_mlt":[{"interim":"Article"}]},"item_1583159729339":{"attribute_name":"Publication date","attribute_value":"2017-02-16"},"item_1583159847033":{"attribute_name":"Identifier","attribute_value":"http://onlineresource.ucsy.edu.mm/handle/123456789/888"},"item_title":"Automatic Myanmar News Classification","item_type_id":"21","owner":"1","path":["1597824273898"],"publish_date":"2019-07-15","publish_status":"0","recid":"4990","relation_version_is_last":true,"title":["Automatic Myanmar News Classification"],"weko_creator_id":"1","weko_shared_id":-1},"updated":"2021-12-13T02:42:19.914495+00:00"}