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 |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Design an Empirical Framework for Sentiment Analysis.pdf | 238.16 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.