{"_buckets": {"deposit": "8d67f194-84c7-462b-9faa-790cdb72f791"}, "_deposit": {"created_by": 45, "id": "5355", "owner": "45", "owners": [45], "owners_ext": {"displayname": "", "username": ""}, "pid": {"revision_id": 0, "type": "recid", "value": "5355"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/5355", "sets": ["user-uit"]}, "communities": ["uit"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Semi-supervised Event Message Identification System for Targeted Domain", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Social media have become increasingly popular\ncomponents of our everyday lives in today’s globalizing society.\nThey provide a context where people across the world can\ncommunicate, exchange messages, share knowledge, and\ninteract with each other regardless of the distance that\nseparates them. This research trend, extraction of events for\nspecific domain from these social media is emerging speedily\nranging from business intelligence to nation security field. The\nshort length of Twitter messages and frequent use of informal\nand ungrammatical language challenge many long standing\napproaches for automatically detecting and categorizing events\nusing streamed data in Event Message Identification system. A\nsemi-supervised approach with Support Vector Machine (SVM)\nin combination with the corpus to identify the events from\ntwitter for targeted domain in specific location is proposed in\nthis paper. The experimental results show that the proposed\nsemi-supervised SVM model is more efficient than a strong\nstate-of-the-art semi-supervised classification model of Logic\nRegression, Naïve Bayes and Decision Tree."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Social media"}, {"interim": "Twitter"}, {"interim": "Semi-supervised"}, {"interim": "Events"}, {"interim": "SVM"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_no", "date": [{"dateType": "Available", "dateValue": "2020-09-14"}], "displaytype": "preview", "filename": "Semi-supervised Event Message Identification System for Targeted Domain.pdf", "filesize": [{"value": "424 Kb"}], "format": "application/pdf", "licensefree": "©2018 IEEE", "licensetype": "license_free", "url": {"url": "https://meral.edu.mm/record/5355/files/Semi-supervised Event Message Identification System for Targeted Domain.pdf"}, "version_id": "177682fe-50ba-4f6a-8aa9-f8b726cc52e6"}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "IEEE, ICTT", "subitem_c_date": "8-11 October, 2018", "subitem_conference_title": "18th IEEE International Conference on Communication Technology", "subitem_place": "China"}]}, "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": "2018-10-11"}, "item_title": "Semi-supervised Event Message Identification System for Targeted Domain", "item_type_id": "21", "owner": "45", "path": ["1596102391527"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000005355", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2020-09-14"}, "publish_date": "2020-09-14", "publish_status": "0", "recid": "5355", "relation": {}, "relation_version_is_last": true, "title": ["Semi-supervised Event Message Identification System for Targeted Domain"], "weko_shared_id": -1}