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...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2022/6614730 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832549433673252864 |
|---|---|
| author | Abdullah Al-Hashedi Belal Al-Fuhaidi Abdulqader M. Mohsen Yousef Ali Hasan Ali Gamal Al-Kaf Wedad Al-Sorori Naseebah Maqtary |
| author_facet | Abdullah Al-Hashedi Belal Al-Fuhaidi Abdulqader M. Mohsen Yousef Ali Hasan Ali Gamal Al-Kaf Wedad Al-Sorori Naseebah Maqtary |
| author_sort | Abdullah Al-Hashedi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3e555559e57c469a9e2034b004704ae2 |
| institution | Kabale University |
| issn | 1687-9732 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-3e555559e57c469a9e2034b004704ae22025-02-03T06:11:18ZengWileyApplied Computational Intelligence and Soft Computing1687-97322022-01-01202210.1155/2022/6614730Ensemble Classifiers for Arabic Sentiment Analysis of Social Network (Twitter Data) towards COVID-19-Related Conspiracy TheoriesAbdullah Al-Hashedi0Belal Al-Fuhaidi1Abdulqader M. Mohsen2Yousef Ali3Hasan Ali Gamal Al-Kaf4Wedad Al-Sorori5Naseebah Maqtary6Faculty of Computing and ITFaculty of Computing and ITFaculty of Computing and ITFaculty of Computing and ITFaculty of Computing and ITFaculty of Computing and ITFaculty of Computing and ITSentiment 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.http://dx.doi.org/10.1155/2022/6614730 |
| spellingShingle | Abdullah Al-Hashedi Belal Al-Fuhaidi Abdulqader M. Mohsen Yousef Ali Hasan Ali Gamal Al-Kaf Wedad Al-Sorori Naseebah Maqtary Ensemble Classifiers for Arabic Sentiment Analysis of Social Network (Twitter Data) towards COVID-19-Related Conspiracy Theories Applied Computational Intelligence and Soft Computing |
| title | Ensemble Classifiers for Arabic Sentiment Analysis of Social Network (Twitter Data) towards COVID-19-Related Conspiracy Theories |
| title_full | Ensemble Classifiers for Arabic Sentiment Analysis of Social Network (Twitter Data) towards COVID-19-Related Conspiracy Theories |
| title_fullStr | Ensemble Classifiers for Arabic Sentiment Analysis of Social Network (Twitter Data) towards COVID-19-Related Conspiracy Theories |
| title_full_unstemmed | Ensemble Classifiers for Arabic Sentiment Analysis of Social Network (Twitter Data) towards COVID-19-Related Conspiracy Theories |
| title_short | Ensemble Classifiers for Arabic Sentiment Analysis of Social Network (Twitter Data) towards COVID-19-Related Conspiracy Theories |
| title_sort | ensemble classifiers for arabic sentiment analysis of social network twitter data towards covid 19 related conspiracy theories |
| url | http://dx.doi.org/10.1155/2022/6614730 |
| work_keys_str_mv | AT abdullahalhashedi ensembleclassifiersforarabicsentimentanalysisofsocialnetworktwitterdatatowardscovid19relatedconspiracytheories AT belalalfuhaidi ensembleclassifiersforarabicsentimentanalysisofsocialnetworktwitterdatatowardscovid19relatedconspiracytheories AT abdulqadermmohsen ensembleclassifiersforarabicsentimentanalysisofsocialnetworktwitterdatatowardscovid19relatedconspiracytheories AT yousefali ensembleclassifiersforarabicsentimentanalysisofsocialnetworktwitterdatatowardscovid19relatedconspiracytheories AT hasanaligamalalkaf ensembleclassifiersforarabicsentimentanalysisofsocialnetworktwitterdatatowardscovid19relatedconspiracytheories AT wedadalsorori ensembleclassifiersforarabicsentimentanalysisofsocialnetworktwitterdatatowardscovid19relatedconspiracytheories AT naseebahmaqtary ensembleclassifiersforarabicsentimentanalysisofsocialnetworktwitterdatatowardscovid19relatedconspiracytheories |