{"_buckets": {"deposit": "2f916055-47c1-4099-ae0d-296c6344f57c"}, "_deposit": {"id": "4470", "owners": [], "pid": {"revision_id": 0, "type": "recid", "value": "4470"}, "status": "published"}, "_oai": {"id": "oai:meral.edu.mm:recid/4470", "sets": ["user-ucsy"]}, "communities": ["ucsy"], "item_1583103067471": {"attribute_name": "Title", "attribute_value_mlt": [{"subitem_1551255647225": "Multi-tier Sentiment Analysis System with Sarcasm Detection: A Big Data Approach", "subitem_1551255648112": "en"}]}, "item_1583103085720": {"attribute_name": "Description", "attribute_value_mlt": [{"interim": "Social Media is one of the generating sourcesof big data and analyzing social big data can providethe valuable information. For analyzing the socialbig data in an efficient and timely manner, thetraditional analytic platform is needed to be scaledup. The powerful technique is necessary to extract thevaluable information from social big data. SentimentAnalysis can facilitate valuable information byextracting public opinions. The presence of sarcasm,an interfering factor that can flip the sentiment of thegiven text, is one of the challenges of SentimentAnalysis. In this paper, Multi-tier Sentiment Analysissystem with sarcasm detection on Hadoop(MSASDH) is proposed to extract the opinion fromlarge volumes of tweets. To achieve high-levelperformance of sentiment classification, MSASDHidentifies sarcasm and sentiment-emotion byconducting rule based sarcasm-sentiment detectionscheme and learning based sentiment classificationwith Multi-tier architecture. The large amount oftweets is collected by Apache Flume and it is used forsystem evaluation. The evaluation results show thatdetecting sarcasm can enhance the accuracy ofSentiment Analysis. Moreover, the results show thatthe MSASDH is efficient and scalable by decreasingthe processing time when adding more nodes into thecluster."}]}, "item_1583103108160": {"attribute_name": "Keywords", "attribute_value_mlt": [{"interim": "Big data"}, {"interim": "Hadoop"}, {"interim": "Machine Learning"}, {"interim": "Sarcasm"}, {"interim": "Sentiment Analysis"}, {"interim": "Tweets"}]}, "item_1583103120197": {"attribute_name": "Files", "attribute_type": "file", "attribute_value_mlt": [{"accessrole": "open_access", "date": [{"dateType": "Available", "dateValue": "2019-07-03"}], "displaytype": "preview", "download_preview_message": "", "file_order": 0, "filename": "40-49.pdf", "filesize": [{"value": "277 Kb"}], "format": "application/pdf", "future_date_message": "", "is_thumbnail": false, "licensetype": "license_free", "mimetype": "application/pdf", "size": 277000.0, "url": {"url": "https://meral.edu.mm/record/4470/files/40-49.pdf"}, "version_id": "bcce02ed-1852-49ca-b4fb-76eed8cd46f0"}]}, "item_1583103131163": {"attribute_name": "Journal articles", "attribute_value_mlt": [{"subitem_issue": "", "subitem_journal_title": "Sixteenth International Conferences on Computer Applications(ICCA 2018)", "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": "Chan, Wint Nyein"}, {"subitem_authors_fullname": "Thein, Thandar"}]}]}, "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": "2018-02-22"}, "item_1583159847033": {"attribute_name": "Identifier", "attribute_value": "http://onlineresource.ucsy.edu.mm/handle/123456789/240"}, "item_title": "Multi-tier Sentiment Analysis System with Sarcasm Detection: A Big Data Approach", "item_type_id": "21", "owner": "1", "path": ["1597824273898"], "permalink_uri": "http://hdl.handle.net/20.500.12678/0000004470", "pubdate": {"attribute_name": "Deposited date", "attribute_value": "2019-07-03"}, "publish_date": "2019-07-03", "publish_status": "0", "recid": "4470", "relation": {}, "relation_version_is_last": true, "title": ["Multi-tier Sentiment Analysis System with Sarcasm Detection: A Big Data Approach"], "weko_shared_id": -1}