{"_buckets": {"deposit": "9d96ed06-1483-4faf-ac5c-0a4297d8a68a"}, "_deposit": {"created_by": 45, "id": "5353", "owner": "45", "owners": [45], "owners_ext": {"displayname": "", "username": ""}, "pid": {"revision_id": 0, "type": "recid", "value": "5353"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/5353", "sets": ["1596102391527", "user-uit"]}, "communities": ["uit"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Information Extraction from Social Media", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "With the proliferation of social media\nsites, such as Twitter, Facebook, and LinkedIn,\nsocial streams have proven to contain the most\nup-to-date information on current events.\nTherefore, it is crucial to extract activities or\nevents from the social streams, such as tweets\nand it become an ongoing research trend. Most\napproaches that aim at extracting event\ninformation from twitter typically use the context\nof messages. However, exploiting the location\ninformation of geo-referenced messages and the\nprofile data are also important because tweet\nmessages are short, fragmented and noisy, and\ntherefore not include complete information about\nthe events. For this, in this paper, a framework\nfor event-extraction and categorization from\nTwitter is proposed. To extract the localized\nrelated activities, several mining mechanisms\nand cleaning techniques is used for real-time\ntwitter corpus and various language processing\napproaches is applied for categorization the\nevents and then the system will display the\nvaluable information for the targeted domain."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Information Extraction"}, {"interim": "Social Media"}, {"interim": "Activity"}, {"interim": "Event"}, {"interim": "Twitter"}]}, "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": "Information Extraction from Social Media.pdf", "filesize": [{"value": "194 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_0", "mimetype": "application/pdf", "size": 194000.0, "url": {"url": "https://meral.edu.mm/record/5353/files/Information Extraction from Social Media.pdf"}, "version_id": "9f3df76e-9ebe-48c6-b365-5432fb5b466c"}]}, "item_1583103147082": {"attribute_name": "Conference papers", "attribute_value_mlt": [{"subitem_acronym": "ICCA", "subitem_c_date": "16-17 February, 2017", "subitem_conference_title": "International Conference on Computer Applications", "subitem_place": "Sedona Hotel, Yangon, Myanmar"}]}, "item_1583105942107": {"attribute_name": "Authors", "attribute_value_mlt": [{"subitem_authors": [{"subitem_authors_fullname": "San San Nwe"}, {"subitem_authors_fullname": "Khin Nwe Ni Tun"}]}]}, "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": "2017-02-17"}, "item_title": "Information Extraction from Social Media", "item_type_id": "21", "owner": "45", "path": ["1596102391527"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000005353", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2020-09-14"}, "publish_date": "2020-09-14", "publish_status": "0", "recid": "5353", "relation": {}, "relation_version_is_last": true, "title": ["Information Extraction from Social Media"], "weko_shared_id": -1}