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Design an Empirical Framework for Sentiment Analysis from Bangla Text using Machine Learning

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dc.contributor.author Tabassum, Nusrath
dc.contributor.author Khan, Muhammad Ibrahim
dc.date.accessioned 2021-09-27T10:12:16Z
dc.date.available 2021-09-27T10:12:16Z
dc.date.issued 2019-02-07
dc.identifier.isbn 978-1-5386-9111-3
dc.identifier.uri http://103.99.128.19:8080/xmlui/handle/123456789/292
dc.description.abstract Natural 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.iso en_US en_US
dc.publisher Faculty of Electrical and Computer Engineering, CUET en_US
dc.relation.ispartofseries ECCE;
dc.subject Bangla language en_US
dc.subject Feature extraction en_US
dc.subject Sentiment inspection en_US
dc.title Design an Empirical Framework for Sentiment Analysis from Bangla Text using Machine Learning en_US
dc.title.alternative International Conference on Electrical, Computer and Communication Engineering (ECCE-2019) en_US
dc.type Article en_US


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