Facebook Comments-Based Assessment Model for Food Product Companies Using Four Machine Learning Methods with Arabic/Iraqi Lexicon
DOI:
https://doi.org/10.21123/bsj.2024.10099Keywords:
Assessment Model, Facebook Comments, Food Product, Iraqi Dialect Machine Learning.Abstract
People's need for food is increasing day after day due to the large population increase that has occurred in human society, which has led to a significant increase in the activity of food product manufacturing companies in the world. Hence the necessary need to evaluate the performance of food production companies. We found that the best way to evaluate the performance of these companies is through social media comments, because it is the newest and most common method among people. In this paper, 4 machine learning techniques were relied upon to evaluate participants’ comments in evaluating food production companies after using a lexicon of Arabic language terms in general and the Iraqi dialect in particular. These terms relate to positive and negative words and expressions. Then training through machine learning algorithms. Random Forest (RF), Naive Bayes (NB), Rough Set Theory (RST), and Support Vector Machine (SVM) algorithms were tested. Experiments have shown that RST is superior to the other three. RST achieved (96.13%), SVM achieved (95.75%), although RF achieved (94.1%) and NB achieved (87.1%).
Received 02/11/2023
Revised 12/07/2024
Accepted 14/07/2024
Published Online First 20/11/2024
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Copyright (c) 2024 Raad Sadi Aziz, Sura Mazin Ali, Ahmed T. Sadiq
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