Improving Sentiment Analysis of Arabic Tweets by One-way ANOVA
Social media is an indispensable necessity for modern life. As a result, it is full of people’s opinions, emotions, ideas, and attitudes, whether positive or negative. This abundance of views creates many opportunities for applying sentiment analysis to the education sector, which reflects how count...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-06-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157820305176 |
| _version_ | 1811334717289529344 |
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| author | Manar Alassaf Ali Mustafa Qamar |
| author_facet | Manar Alassaf Ali Mustafa Qamar |
| author_sort | Manar Alassaf |
| collection | DOAJ |
| description | Social media is an indispensable necessity for modern life. As a result, it is full of people’s opinions, emotions, ideas, and attitudes, whether positive or negative. This abundance of views creates many opportunities for applying sentiment analysis to the education sector, which reflects how countries and cultures develop. In this research, a real-world Twitter dataset was collected, containing approximately 8144 tweets related to Qassim University, Saudi Arabia. The main aim of this experimental study was to explore the possibility of using a one-way analysis of variance (ANOVA) as a feature selection method to considerably reduce the number of features when classifying opinions conveyed through Arabic tweets. The primary motivation for this research was that no previous studies had examined one-way ANOVA comprehensively to tackle the curse of dimensionality and to enhance classification performance in sentiment analysis for Arabic tweets. Therefore, various experiments were conducted to investigate the effects of one-way ANOVA and to select important features concerning the performance of different supervised machine learning classifiers. Support Vector Machine and Naïve Bayes achieved the best results with one-way ANOVA as compared to the baseline experimental results in the collected dataset. Furthermore, the differences between all results have been statistically analyzed in this study. As further evidence, one-way ANOVA with Support Vector Machine represented an excellent combination across different Arabic benchmark datasets, with its results outperforming other studies. |
| first_indexed | 2024-04-13T17:13:17Z |
| format | Article |
| id | doaj.art-dad35007da014a8b9a7de18b967ec94c |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-04-13T17:13:17Z |
| publishDate | 2022-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-dad35007da014a8b9a7de18b967ec94c2022-12-22T02:38:13ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782022-06-0134628492859Improving Sentiment Analysis of Arabic Tweets by One-way ANOVAManar Alassaf0Ali Mustafa Qamar1Corresponding author.; Department of Computer Science, College of Computer, Qassim University, Buraydah, Saudi ArabiaDepartment of Computer Science, College of Computer, Qassim University, Buraydah, Saudi ArabiaSocial media is an indispensable necessity for modern life. As a result, it is full of people’s opinions, emotions, ideas, and attitudes, whether positive or negative. This abundance of views creates many opportunities for applying sentiment analysis to the education sector, which reflects how countries and cultures develop. In this research, a real-world Twitter dataset was collected, containing approximately 8144 tweets related to Qassim University, Saudi Arabia. The main aim of this experimental study was to explore the possibility of using a one-way analysis of variance (ANOVA) as a feature selection method to considerably reduce the number of features when classifying opinions conveyed through Arabic tweets. The primary motivation for this research was that no previous studies had examined one-way ANOVA comprehensively to tackle the curse of dimensionality and to enhance classification performance in sentiment analysis for Arabic tweets. Therefore, various experiments were conducted to investigate the effects of one-way ANOVA and to select important features concerning the performance of different supervised machine learning classifiers. Support Vector Machine and Naïve Bayes achieved the best results with one-way ANOVA as compared to the baseline experimental results in the collected dataset. Furthermore, the differences between all results have been statistically analyzed in this study. As further evidence, one-way ANOVA with Support Vector Machine represented an excellent combination across different Arabic benchmark datasets, with its results outperforming other studies.http://www.sciencedirect.com/science/article/pii/S1319157820305176Sentiment analysisOne-way ANOVAArabic tweetsFeature selectionMachine learningHigh dimensionality |
| spellingShingle | Manar Alassaf Ali Mustafa Qamar Improving Sentiment Analysis of Arabic Tweets by One-way ANOVA Journal of King Saud University: Computer and Information Sciences Sentiment analysis One-way ANOVA Arabic tweets Feature selection Machine learning High dimensionality |
| title | Improving Sentiment Analysis of Arabic Tweets by One-way ANOVA |
| title_full | Improving Sentiment Analysis of Arabic Tweets by One-way ANOVA |
| title_fullStr | Improving Sentiment Analysis of Arabic Tweets by One-way ANOVA |
| title_full_unstemmed | Improving Sentiment Analysis of Arabic Tweets by One-way ANOVA |
| title_short | Improving Sentiment Analysis of Arabic Tweets by One-way ANOVA |
| title_sort | improving sentiment analysis of arabic tweets by one way anova |
| topic | Sentiment analysis One-way ANOVA Arabic tweets Feature selection Machine learning High dimensionality |
| url | http://www.sciencedirect.com/science/article/pii/S1319157820305176 |
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