{"created":"2020-11-19T15:24:01.656361+00:00","id":6270,"links":{},"metadata":{"_buckets":{"deposit":"e9ff8128-81be-439a-adf6-81660e28e461"},"_deposit":{"created_by":45,"id":"6270","owner":"45","owners":[45],"owners_ext":{"displayname":"","email":"dimennyaung@uit.edu.mm","username":""},"pid":{"revision_id":0,"type":"recid","value":"6270"},"status":"published"},"_oai":{"id":"oai:meral.edu.mm:recid/6270","sets":["1582963342780:1605779935331"]},"communities":["uit"],"item_1583103067471":{"attribute_name":"Title","attribute_value_mlt":[{"subitem_1551255647225":"Domain-specific Sentiment Dictionary Construction for Sentiment Classification","subitem_1551255648112":"en"}]},"item_1583103085720":{"attribute_name":"Description","attribute_value_mlt":[{"interim":"Sentiment dictionaries are commonly used to solve the problem of sentiment classification for customer reviews. The number of sentiment words in the generalized dictionaries such as SentiWordNet is limited and lack of many sentiment words especially domain-specific sentiment words. Different domains have different sentiment words and the sentiment of a word depends on the domain in which it is used. In this paper, an approach based on Point-wise Mutual Information (PMI) is proposed to construct a domain-specific sentiment dictionary effectively and automatically. The proposed system is evaluated on three diverse datasets from different domains by using 10-fold cross validation. Accordingly to the experimental results, the goodness of the extracted dictionary is relatively high and significantly improves the performance of sentiment classification. The experimental results show that the extracted domain-specific dictionary outperforms the generalized dictionary, SentiWordNet. The proposed method learns the domain-specific sentiment words efficiently and it is domain adaptable."}]},"item_1583103108160":{"attribute_name":"Keywords","attribute_value_mlt":[{"interim":"Sentiment Analysis"},{"interim":"Polarity Classification"},{"interim":"Sentiment Dictionary"},{"interim":"Domain-specific Sentiment Words"},{"interim":"Point-wise Mutual Information"}]},"item_1583103120197":{"attribute_name":"Files","attribute_type":"file","attribute_value_mlt":[{"accessrole":"open_access","date":[{"dateType":"Available","dateValue":"2020-11-19"}],"displaytype":"preview","filename":"Domain-specific Sentiment Dictionary Construction for Sentiment Classification.pdf","filesize":[{"value":"1.4 Mb"}],"format":"application/pdf","license_note":"© 2017 ICAIT","licensetype":"license_note","url":{"url":"https://meral.edu.mm/record/6270/files/Domain-specific Sentiment Dictionary Construction for Sentiment Classification.pdf"},"version_id":"a52b2bda-513b-4271-9024-c54fb63e3f9f"}]},"item_1583103147082":{"attribute_name":"Conference papers","attribute_value_mlt":[{"subitem_acronym":"ICAIT-2017","subitem_c_date":"1-2 November, 2017","subitem_conference_title":"1st International Conference on Advanced Information Technologies","subitem_place":"Yangon, Myanmar","subitem_session":"Natural Language Processing","subitem_website":"https://www.uit.edu.mm/icait-2017/"}]},"item_1583105942107":{"attribute_name":"Authors","attribute_value_mlt":[{"subitem_authors":[{"subitem_authors_fullname":"Aye Aye Mar"},{"subitem_authors_fullname":"Nyein Thwet Thwet Aung"},{"subitem_authors_fullname":"Su Su Htay"}]}]},"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":"2017-11-02"},"item_title":"Domain-specific Sentiment Dictionary Construction for Sentiment Classification","item_type_id":"21","owner":"45","path":["1605779935331"],"publish_date":"2020-11-19","publish_status":"0","recid":"6270","relation_version_is_last":true,"title":["Domain-specific Sentiment Dictionary Construction for Sentiment Classification"],"weko_creator_id":"45","weko_shared_id":-1},"updated":"2021-12-13T05:26:09.678048+00:00"}