{"_buckets": {"deposit": "bf35c153-646a-4c82-8d06-f6454381ef4f"}, "_deposit": {"created_by": 45, "id": "6852", "owner": "45", "owners": [45], "owners_ext": {"displayname": "", "username": ""}, "pid": {"revision_id": 0, "type": "depid", "value": "6852"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/00006852", "sets": ["user-uit"]}, "communities": ["uit"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Sentiment Analysis System for Myanmar News using K Nearest Neighbor and Naïve Bayes", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "With the explosive growth of internet technology, there are very large amount of information on the web for the internet users. Users not only use that information but also provide opinions for decision making process. Sentiment analysis or opinion mining is one of text classification techniques that identify and extract opinion described in a piece of text. Our aims in this paper are to develop automatic sentiment analysis system for Myanmar news and to annotate sentiment news. Therefore, this system creates sentiment annotated corpus for Myanmar news. Feature extraction and selection are very important for sentiment analysis to get higher performance. N-grams, Countvectorizer, and TF-IDF are used for feature selection and feature extraction. In this system, Myanmar news sentiment analysis system is implemented by using K Nearest Neighbor (KNN) and Naïve Bayes machine learning algorithms."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Sentiment analysis"}, {"interim": "Naïve Bayes"}, {"interim": "K Nearest Neighbor"}, {"interim": "N-gram"}, {"interim": "TF-IDF"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2020-12-15"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "Sentiment Analysis System for Myanmar News using K Nearest Neighbor and Naïve Bayes.pdf", "filesize": [{"value": "318 KB"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "mimetype": "application/pdf", "size": 318000.0, "url": {"url": "https://meral.edu.mm/record/6852/files/Sentiment Analysis System for Myanmar News using K Nearest Neighbor and Naïve Bayes.pdf"}, "version_id": "16d0b4c4-0af5-4141-ab09-15003893f292"}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "WCSE 2020", "subitem_c_date": "26 February - 28 February, 2020", "subitem_conference_title": "The 10th International Workshop on Computer Science and Engineering"}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "Thein Yu"}, {"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": "2020-02-28"}, "item_title": "Sentiment Analysis System for Myanmar News using K Nearest Neighbor and Naïve Bayes", "item_type_id": "21", "owner": "45", "path": ["1596102355557"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000006852", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2020-12-15"}, "publish_date": "2020-12-15", "publish_status": "0", "recid": "6852", "relation": {}, "relation_version_is_last": true, "title": ["Sentiment Analysis System for Myanmar News using K Nearest Neighbor and Naïve Bayes"], "weko_shared_id": -1}