Please use this identifier to cite or link to this item: http://103.99.128.19:8080/xmlui/handle/123456789/292
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dc.contributor.authorTabassum, Nusrath-
dc.contributor.authorKhan, Muhammad Ibrahim-
dc.date.accessioned2021-09-27T10:12:16Z-
dc.date.available2021-09-27T10:12:16Z-
dc.date.issued2019-02-07-
dc.identifier.isbn978-1-5386-9111-3-
dc.identifier.urihttp://103.99.128.19:8080/xmlui/handle/123456789/292-
dc.description.abstractNatural Language Processing (NLP) lends a helping hand for programming the computers to inspect a huge amount of data. Sentiment analysis is an application of NLP which deals with data to examine the sentiment or opinion that can be either positive or negative. Using Bangla text, sentiment analysis has become a challenge as there were only few works on it. As a decision maker, sentiment extrication not only capturing consumer attitudes but also helps in social behavior observance, politics and policy making. This paper quantifies total positivity and negativity against a document or sentence using Random Forest Classifier to classify sentiments. We contemplate the use of unigram, POS tagging, negation handling and classifier.en_US
dc.language.isoen_USen_US
dc.publisherFaculty of Electrical and Computer Engineering, CUETen_US
dc.relation.ispartofseriesECCE;-
dc.subjectBangla languageen_US
dc.subjectFeature extractionen_US
dc.subjectSentiment inspectionen_US
dc.titleDesign an Empirical Framework for Sentiment Analysis from Bangla Text using Machine Learningen_US
dc.title.alternativeInternational Conference on Electrical, Computer and Communication Engineering (ECCE-2019)en_US
dc.typeArticleen_US
Appears in Collections:proceedings in CSE

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