Arabic Sentiment Analysis on Chewing Khat Leaves using Machine Learning and Ensemble Methods
Received: 5 January 2021 | Revised: 25 January 2021 | Accepted: 28 January 2021 | Online: 15 February 2021
Corresponding author: W. M. S. Yafooz
Abstract
Sentiment analysis plays an important role in obtaining speakers' opinions or feelings towards events, products, topics, or services, helping businesses to improve their products. Moreover, governments and organizations investigate and solve current social issues by analyzing perspectives and feelings. This study evaluated the habit of chewing Khat (qat) leaves among the Yemeni society. Chewing Khat plant leaves, is a common habit in Yemen and East Africa. This paper proposes a model to detect information about the Khat chewing habit, how people explore it, and the preference for Khat leaves among Arabic people. A dataset consisting of user comments on 18 youtube videos was prepared through several natural language processing techniques. Several experiments were conducted using six machine learning classifiers and four ensemble methods. Support Vector Machine and Linear Regression had almost 80% accuracy, whereas xgboot was the most accurate ensemble method reaching 77%.
Keywords:
sentiment analysis, machine learning, classification, ensemble methodsDownloads
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