TY  - CONF
ID  - scholars15365
SP  - 101
TI  - Enhanced Sentiment Analysis Technique using Machine Learning (B.R.A.G.E technique)
KW  - Bayesian networks; Classifiers; Supervised learning
KW  -  Analysis models; Analysis techniques; Detection levels; Feature level; Machine-learning; Naive bayes; Sentence features; Sentence level; Sentiment analysis; Supervised machine learning
KW  -  Sentiment analysis
N1  - cited By 0; Conference of 2021 International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2021 ; Conference Date: 1 December 2021 Through 2 December 2021; Conference Code:176965
N2  - Sentiment Analysis have been the most growing topic in the recent years. It is the use of text analysis to examine the opinion or attitude towards a topic. In the past years, there have been a significant growth in the volume of research on Sentiment Analysis, on different detection level such as document level, sentence level and feature level. One of the famous existing sentiment analysis models is Naïve Bayes, a supervised machine learning model. In this study, we identified that the existing Naïve Bayes model trained and tested with incident/accident-related dataset gave an accuracy level of 71. Additionally, this study describes how the proposed B.R.A.GE. technique has slightly enhanced the sentiment analysis prediction accuracy using incident/accident-related dataset. In conclusion, the proposed B.R.A.G.E technique has not significantly improved the accuracy but hence could be further improvised. © 2021 IEEE.
AV  - none
EP  - 105
A1  - Mohamad, D.E.D.
A1  - Hashim, A.S.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85126617274&doi=10.1109%2fICICyTA53712.2021.9689171&partnerID=40&md5=dac5233151643c1c5b44deb046734317
PB  - Institute of Electrical and Electronics Engineers Inc.
SN  - 9781665417778
Y1  - 2021///
ER  -