{"_buckets": {"deposit": "3ff288b1-c12d-42df-b3e9-b64cee12dc49"}, "_deposit": {"id": "4603", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "4603"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/4603", "sets": ["user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Analysis of Word Vector Representation Techniques with Machine-Learning Classifiers for Sentiment Analysis of Public Facebook Page’s Comments in Myanmar Text", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "This paper presents a study of comparison onthree different machine learning techniques tosentiment analysis for Myanmar language. Thefundamental part of sentiment analysis (SA) is toextract and identify the subjective information that issocial sentiment in the source text. The sentiment classis positive, neutral or negative of a comment. Theexperiments are done on the collected 10,000Facebook comments in Myanmar language. Theobjective of this study is to increase the accuracy ofthe sentiment identification by using the concept ofword embeddings. Word2Vec is used to train forproducing high-dimensional word vectors that learnsthe syntactic and semantic of word. The resulting wordvectors train Machine Learning algorithms in the formof classifiers for sentiment identification. Thisexperimental results prove that the use of wordembeddings from the collected real world datasetsimproved the accuracy of sentiments classificationand Logistic Regression outperformed the other twoML methods in terms of accuracy and F-measures."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Multiclass classification"}, {"interim": "natural language processing"}, {"interim": "sentiment analysis"}, {"interim": "Facebook Page\u0027s comments"}, {"interim": "word embedding"}, {"interim": "Logistic Regression"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2020-03-17"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "Analysis of Word Vector Representation Techniques with Machine-Learning Classifiers for Sentiment Analysis of Public Facebook Page’s Comments in Myanmar Text.pdf", "filesize": [{"value": "0 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 0.0, "url": {"url": "https://meral.edu.mm/record/4603/files/Analysis of Word Vector Representation Techniques with Machine-Learning Classifiers for Sentiment Analysis of Public Facebook Page’s Comments in Myanmar Text.pdf"}, "version_id": "910bbc55-6383-4631-a6c7-a993d9e2b5a6"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "Proceedings of the Eighteenth International Conference On Computer Applications (ICCA 2020)", "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": "Aung, Hay Mar Su"}, {"subitem_authors_fullname": "Pa, Win Pa"}]}]}, "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": "2020-02-28"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "978-1-7281-5925-6"}, "item_title": "Analysis of Word Vector Representation Techniques with Machine-Learning Classifiers for Sentiment Analysis of Public Facebook Page’s Comments in Myanmar Text", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000004603", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2020-03-17"}, "publish_date": "2020-03-17", "publish_status": "0", "recid": "4603", "relation": {}, "relation_version_is_last": true, "title": ["Analysis of Word Vector Representation Techniques with Machine-Learning Classifiers for Sentiment Analysis of Public Facebook Page’s Comments in Myanmar Text"], "weko_shared_id": -1}