Ensemble Classifiers for Arabic Sentiment Analysis of Social Network (Twitter Data) towards COVID-19-Related Conspiracy Theories

Sentiment analysis has recently become increasingly important with a massive increase in online content. It is associated with the analysis of textual data generated by social media that can be easily accessed, obtained, and analyzed. With the emergence of COVID-19, most published studies related to...

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Bibliographic Details
Published in:Applied Computational Intelligence and Soft Computing Vol. 2022; pp. 1 - 10
Main Authors: Al-Hashedi, Abdullah, Al-Fuhaidi, Belal, Mohsen, Abdulqader M., Ali, Yousef, Gamal Al-Kaf, Hasan Ali, Al-Sorori, Wedad, Maqtary, Naseebah
Format: Journal Article
Language:English
Published: New York Hindawi 13.01.2022
John Wiley & Sons, Inc
Wiley
Subjects:
Classifiers
Conspiracy theories
Coronaviruses
Data mining
Digital media
Embedding
English language
Ensemble learning
Epidemics
Gates, Bill
Machine learning
Mediation
Oversampling
Pandemics
Sentiment analysis
Social media
Social networks
United Kingdom
Words (language)
United Kingdom
United Kingdom > UK
England
ISSN:1687-9724, 1687-9732
Online Access:Get full text
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Summary:Sentiment analysis has recently become increasingly important with a massive increase in online content. It is associated with the analysis of textual data generated by social media that can be easily accessed, obtained, and analyzed. With the emergence of COVID-19, most published studies related to COVID-19’s conspiracy theories were surveys on the people's sentiments and opinions and studied the impact of the pandemic on their lives. Just a few studies utilized sentiment analysis of social media using a machine learning approach. These studies focused more on sentiment analysis of Twitter tweets in the English language and did not pay more attention to other languages such as Arabic. This study proposes a machine learning model to analyze the Arabic tweets from Twitter. In this model, we apply Word2Vec for word embedding which formed the main source of features. Two pretrained continuous bag-of-words (CBOW) models are investigated, and Naïve Bayes was used as a baseline classifier. Several single-based and ensemble-based machine learning classifiers have been used with and without SMOTE (synthetic minority oversampling technique). The experimental results show that applying word embedding with an ensemble and SMOTE achieved good improvement on average of F1 score compared to the baseline classifier and other classifiers (single-based and ensemble-based) without SMOTE.
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ISSN:1687-9724
1687-9732
DOI:10.1155/2022/6614730