TY - JOUR
T1 - Emotion Recognition by Textual Tweets Classification Using Voting Classifier (LR-SGD)
AU - Yousaf, Anam
AU - Umer, Muhammad
AU - Sadiq, Saima
AU - Ullah, Saleem
AU - Mirjalili, Seyedali
AU - Rupapara, Vaibhav
AU - Nappi, Michele
N1 - Funding Information:
This work was supported by the Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan, Pakistan.
Publisher Copyright:
© 2013 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2021
Y1 - 2021
N2 - The proliferation of user-generated content on social media has made opinion mining an arduous job. As a microblogging platform, Twitter is being used to collect views about products, trends, and politics. Sentiment analysis is a technique used to analyze the attitude, emotions and opinions of different people towards anything, and it can be carried out on tweets to analyze public opinion on news, policies, social movements, and personalities. By employing Machine Learning models, opinion mining can be performed without reading tweets manually. Their results could assist governments and businesses in rolling out policies, products, and events. Seven Machine Learning models are implemented for emotion recognition by classifying tweets as happy or unhappy. With an in-depth comparative performance analysis, it was observed that proposed voting classifier(LR-SGD) with TF-IDF produces the most optimal result with 79% accuracy and 81% F1 score. To further validate stability of the proposed approach on two more datasets, one binary and other multi-class dataset and achieved robust results.
AB - The proliferation of user-generated content on social media has made opinion mining an arduous job. As a microblogging platform, Twitter is being used to collect views about products, trends, and politics. Sentiment analysis is a technique used to analyze the attitude, emotions and opinions of different people towards anything, and it can be carried out on tweets to analyze public opinion on news, policies, social movements, and personalities. By employing Machine Learning models, opinion mining can be performed without reading tweets manually. Their results could assist governments and businesses in rolling out policies, products, and events. Seven Machine Learning models are implemented for emotion recognition by classifying tweets as happy or unhappy. With an in-depth comparative performance analysis, it was observed that proposed voting classifier(LR-SGD) with TF-IDF produces the most optimal result with 79% accuracy and 81% F1 score. To further validate stability of the proposed approach on two more datasets, one binary and other multi-class dataset and achieved robust results.
KW - artificial intelligence
KW - emotion recognition
KW - machine learning
KW - opinion mining
KW - Sentiment analysis
KW - text classification
UR - http://www.scopus.com/inward/record.url?scp=85099080211&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2020.3047831
DO - 10.1109/ACCESS.2020.3047831
M3 - Article
AN - SCOPUS:85099080211
VL - 9
SP - 6286
EP - 6295
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
M1 - 9309291
ER -