Machine Learning Classification Algorithms for Sentiment Analysis in Arabic: Performance Evaluation and Comparison

Ruba Kharsa, Saad Harous

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2022 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages395-400
Number of pages6
ISBN (Electronic)9781665456005
DOIs
Publication statusPublished - 2022
Externally publishedYes
Event2022 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2022 - Ras Al Khaimah, United Arab Emirates
Duration: Nov 23 2022Nov 25 2022

Publication series

Name2022 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2022

Conference

Conference2022 International Conference on Electrical and Computing Technologies and Applications, ICECTA 2022
Country/TerritoryUnited Arab Emirates
CityRas Al Khaimah
Period11/23/2211/25/22

Keywords

  • Classification
  • Machine Learning
  • Sentiment Analysis
  • Time Complexity

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Signal Processing
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Safety, Risk, Reliability and Quality
  • Control and Optimization

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