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 |