{"_buckets": {"deposit": "46eaa463-d6a1-429e-b180-5ab0e5a6dc78"}, "_deposit": {"created_by": 45, "id": "5354", "owner": "45", "owners": [45], "owners_ext": {"displayname": "", "username": ""}, "pid": {"revision_id": 0, "type": "recid", "value": "5354"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/5354", "sets": ["user-uit"]}, "communities": ["uit"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Semi-supervised Domain Specified Event Extraction from Social Media", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Social media has quickly become\npopular as an important means that people,\norganizations use to spread information of divert\nevents for various purposes, ranging from\nbusiness intelligence to nation security.\nHowever, the language used in Twitter is heavily\ninformal, ungrammatical, short and dynamic.\nAutomatically detecting and categorizing events\nusing streamed data is a difficult task, due to the\npresence of noise and irrelevant information.\nTherefore, as an emerging research area, event\nanalysis from social media, Twitter has attracted\nmuch attention since 2010 and there are many\nattempts to detect and categorize events from\nsocial media. This paper proposes a framework\nto identify the events from twitter in a semisupervised\nmanner for targeted domain in\nspecific location with SVM in combination with\nthe corpus. The demonstration shown that, with\nthe selective use of a variety of unlabeled data,\nthe SVM models outperform a strong state-ofthe-\nart supervised classification\nmodel"}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Social Media"}, {"interim": "Twitter"}, {"interim": "Semisupervised"}, {"interim": "Events"}, {"interim": "SVM"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2020-09-14"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "Semi-supervised Domain Specified Event Extraction from Social Media.pdf", "filesize": [{"value": "126 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_0", "mimetype": "application/pdf", "size": 126000.0, "url": {"url": "https://meral.edu.mm/record/5354/files/Semi-supervised Domain Specified Event Extraction from Social Media.pdf"}, "version_id": "3d936596-12f8-45d2-9945-0069b9513cc0"}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "ICCA-2018", "subitem_c_date": "22-23 February, 2018", "subitem_conference_title": "International Conference on Computer Applications", "subitem_place": "Yangon, Myanmar"}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "San San Nwe"}, {"subitem_authors_fullname": "Nang Saing Moon Kham"}]}]}, "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": "2020-09-14"}, "item_title": "Semi-supervised Domain Specified Event Extraction from Social Media", "item_type_id": "21", "owner": "45", "path": ["1596102391527"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000005354", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2020-09-14"}, "publish_date": "2020-09-14", "publish_status": "0", "recid": "5354", "relation": {}, "relation_version_is_last": true, "title": ["Semi-supervised Domain Specified Event Extraction from Social Media"], "weko_shared_id": -1}