TY - GEN
T1 - Machine Learning Classification Algorithms for Sentiment Analysis in Arabic
T2 - 2022 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2022
AU - Kharsa, Ruba
AU - Harous, Saad
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Researchers started utilizing and optimizing the state-of-the-art Machine Learning (ML) and Deep Learning (DL) models to benefit Arabic language tools and applications. They employed social media platforms such as Twitter to gather enormous datasets in the Modern Standard Arabic and Arabic dialects, then used the collected datasets to train their models. This noticeable development in the field needs a detailed comparison study to review the work done and highlight the openings for future contributions and improvements. Based on the conducted review, there exists a gap in the time-complexity evaluation of the used ML algorithms in the field of Arabic Sentiment Analysis. Thus, this study presents an experimental approach for determining the time complexity of seven popular ML algorithms in classifying positive and negative Arabic sentences. The results show that the Multi-Layer Perceptron (MLP) and the Support Vector Machine (SVM) have the highest complexity, whereas the Logistic Regression (LR) has the lowest.
AB - Researchers started utilizing and optimizing the state-of-the-art Machine Learning (ML) and Deep Learning (DL) models to benefit Arabic language tools and applications. They employed social media platforms such as Twitter to gather enormous datasets in the Modern Standard Arabic and Arabic dialects, then used the collected datasets to train their models. This noticeable development in the field needs a detailed comparison study to review the work done and highlight the openings for future contributions and improvements. Based on the conducted review, there exists a gap in the time-complexity evaluation of the used ML algorithms in the field of Arabic Sentiment Analysis. Thus, this study presents an experimental approach for determining the time complexity of seven popular ML algorithms in classifying positive and negative Arabic sentences. The results show that the Multi-Layer Perceptron (MLP) and the Support Vector Machine (SVM) have the highest complexity, whereas the Logistic Regression (LR) has the lowest.
KW - Classification
KW - Machine Learning
KW - Sentiment Analysis
KW - Time Complexity
UR - http://www.scopus.com/inward/record.url?scp=85146366470&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85146366470&partnerID=8YFLogxK
U2 - 10.1109/ICECTA57148.2022.9990108
DO - 10.1109/ICECTA57148.2022.9990108
M3 - Conference contribution
AN - SCOPUS:85146366470
T3 - 2022 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2022
SP - 395
EP - 400
BT - 2022 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 23 November 2022 through 25 November 2022
ER -