MERAL Myanmar Education Research and Learning Portal
Item
{"_buckets": {"deposit": "f1fbd010-2bb8-41ab-b5e7-73469ced34d7"}, "_deposit": {"created_by": 45, "id": "6756", "owner": "45", "owners": [45], "owners_ext": {"displayname": "", "username": ""}, "pid": {"revision_id": 0, "type": "depid", "value": "6756"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/00006756", "sets": ["user-uit"]}, "communities": ["uit"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "A Comparative Study of Machine Learning Algorithms for Myanmar News Classification", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "The aim of this paper is comparative study of\nmachine learning algorithms such as Naïve Bayes (NB), k-nearest\nneighbors (KNN), support vector machine (SVM) algorithms for\nMyanmar Language News classification. Text classification is a\nvery important research area in machine learning. Artificial\nIntelligence is reshaping text classification techniques to better\nacquire knowledge. In spite of the growth and spread of AI in text\nmining research for various languages such as English, Japanese,\nChinese, etc., its role with respect to Myanmar text is not well\nunderstood yet. There is no comparative study of machine learning\nalgorithms in Myanmar News. The news is classified into one of\nfour categories (political, Business, Entertainment and Sport).\nDataset is collected from 1,000 documents belongs to 4 categories.\nWell-known algorithms are applied on collected Myanmar\nlanguage News dataset from websites. The goal of text\nclassification is to classify documents into a certain number of predefined\ncategories. News corpus is used for training and testing\npurpose of the classifier. Feature selection method, TFIDF\nalgorithm achieves comparable performance across a number of\nclassifiers. The results also show support vector machine is better\naccuracy to other classification algorithms employed in this\nresearch. Support vector machine algorithm is better to other\nclassification algorithms employed in this research."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Text classification"}, {"interim": "Feature selection"}, {"interim": "Machine Learning Algorithms"}, {"interim": "Natural Language Processing"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2020-12-12"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "A Comparative Study of Machine Learning Algorithms for Myanmar News Classification.pdf", "filesize": [{"value": "248 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "mimetype": "application/pdf", "size": 248000.0, "url": {"url": "https://meral.edu.mm/api/files/f1fbd010-2bb8-41ab-b5e7-73469ced34d7/A%20Comparative%20Study%20of%20Machine%20Learning%20Algorithms%20for%20Myanmar%20News%20Classification.pdf"}, "version_id": "3c018680-f943-4424-80b4-eac87a5039bc"}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "ICSTSD", "subitem_c_date": "September, 2019", "subitem_conference_title": "International Conference on Science and Technology for Sustainable Development 2019"}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Khin Thandar Nwet"}]}]}, "item_1583108359239": {"attribute_name": "Upload type", "attribute_value_mlt": [{"interim": "Publication"}]}, "item_1583108428133": {"attribute_name": "Publication type", "attribute_value_mlt": [{"interim": "Conference paper"}]}, "item_1583159729339": {"attribute_name": "Publication date", "attribute_value": "2019-09-02"}, "item_title": "A Comparative Study of Machine Learning Algorithms for Myanmar News Classification", "item_type_id": "21", "owner": "45", "path": ["1596102355557"], "permalink_uri": "https://meral.edu.mm/records/6756", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2020-12-12"}, "publish_date": "2020-12-12", "publish_status": "0", "recid": "6756", "relation": {}, "relation_version_is_last": true, "title": ["A Comparative Study of Machine Learning Algorithms for Myanmar News Classification"], "weko_shared_id": -1}
A Comparative Study of Machine Learning Algorithms for Myanmar News Classification
https://meral.edu.mm/records/6756
https://meral.edu.mm/records/6756d0fc4059-132a-42ff-9a80-5dddbe3a4984
f1fbd010-2bb8-41ab-b5e7-73469ced34d7
Name / File | License | Actions |
---|---|---|
A Comparative Study of Machine Learning Algorithms for Myanmar News Classification.pdf (248 Kb)
|
|
Publication type | ||||||
---|---|---|---|---|---|---|
Conference paper | ||||||
Upload type | ||||||
Publication | ||||||
Title | ||||||
Title | A Comparative Study of Machine Learning Algorithms for Myanmar News Classification | |||||
Language | en | |||||
Publication date | 2019-09-02 | |||||
Authors | ||||||
Khin Thandar Nwet | ||||||
Description | ||||||
The aim of this paper is comparative study of machine learning algorithms such as Naïve Bayes (NB), k-nearest neighbors (KNN), support vector machine (SVM) algorithms for Myanmar Language News classification. Text classification is a very important research area in machine learning. Artificial Intelligence is reshaping text classification techniques to better acquire knowledge. In spite of the growth and spread of AI in text mining research for various languages such as English, Japanese, Chinese, etc., its role with respect to Myanmar text is not well understood yet. There is no comparative study of machine learning algorithms in Myanmar News. The news is classified into one of four categories (political, Business, Entertainment and Sport). Dataset is collected from 1,000 documents belongs to 4 categories. Well-known algorithms are applied on collected Myanmar language News dataset from websites. The goal of text classification is to classify documents into a certain number of predefined categories. News corpus is used for training and testing purpose of the classifier. Feature selection method, TFIDF algorithm achieves comparable performance across a number of classifiers. The results also show support vector machine is better accuracy to other classification algorithms employed in this research. Support vector machine algorithm is better to other classification algorithms employed in this research. |
||||||
Keywords | ||||||
Text classification, Feature selection, Machine Learning Algorithms, Natural Language Processing | ||||||
Conference papers | ||||||
ICSTSD | ||||||
September, 2019 | ||||||
International Conference on Science and Technology for Sustainable Development 2019 |