Please use this identifier to cite or link to this item: http://103.99.128.19:8080/xmlui/handle/123456789/292
Title: Design an Empirical Framework for Sentiment Analysis from Bangla Text using Machine Learning
Other Titles: International Conference on Electrical, Computer and Communication Engineering (ECCE-2019)
Authors: Tabassum, Nusrath
Khan, Muhammad Ibrahim
Keywords: Bangla language
Feature extraction
Sentiment inspection
Issue Date: 7-Feb-2019
Publisher: Faculty of Electrical and Computer Engineering, CUET
Series/Report no.: ECCE;
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.
URI: http://103.99.128.19:8080/xmlui/handle/123456789/292
ISBN: 978-1-5386-9111-3
Appears in Collections:proceedings in CSE

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