A machine learning-based approach for sentiment analysis on distance learning from Arabic Tweets
Social media platforms such as Twitter, YouTube, Instagram and Facebook are leading sources of large datasets nowadays. Twitter’s data is one of the most reliable due to its privacy policy. Tweets have been used for sentiment analysis and to identify meaningful information within the dataset. Our st...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2022-07-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-1047.pdf |
| _version_ | 1828778039156146176 |
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| author | Jameel Almalki |
| author_facet | Jameel Almalki |
| author_sort | Jameel Almalki |
| collection | DOAJ |
| description | Social media platforms such as Twitter, YouTube, Instagram and Facebook are leading sources of large datasets nowadays. Twitter’s data is one of the most reliable due to its privacy policy. Tweets have been used for sentiment analysis and to identify meaningful information within the dataset. Our study focused on the distance learning domain in Saudi Arabia by analyzing Arabic tweets about distance learning. This work proposes a model for analyzing people’s feedback using a Twitter dataset in the distance learning domain. The proposed model is based on the Apache Spark product to manage the large dataset. The proposed model uses the Twitter API to get the tweets as raw data. These tweets were stored in the Apache Spark server. A regex-based technique for preprocessing removed retweets, links, hashtags, English words and numbers, usernames, and emojis from the dataset. After that, a Logistic-based Regression model was trained on the pre-processed data. This Logistic Regression model, from the field of machine learning, was used to predict the sentiment inside the tweets. Finally, a Flask application was built for sentiment analysis of the Arabic tweets. The proposed model gives better results when compared to various applied techniques. The proposed model is evaluated on test data to calculate Accuracy, F1 Score, Precision, and Recall, obtaining scores of 91%, 90%, 90%, and 89%, respectively. |
| first_indexed | 2024-12-11T16:34:07Z |
| format | Article |
| id | doaj.art-63c6f7cfead54c689e7f3021edf2c557 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-12-11T16:34:07Z |
| publishDate | 2022-07-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-63c6f7cfead54c689e7f3021edf2c5572022-12-22T00:58:31ZengPeerJ Inc.PeerJ Computer Science2376-59922022-07-018e104710.7717/peerj-cs.1047A machine learning-based approach for sentiment analysis on distance learning from Arabic TweetsJameel Almalki0Department of Computer Science, College of Computer in Al-Leith, Umm Al-Qura University, Makkah, Saudi ArabiaSocial media platforms such as Twitter, YouTube, Instagram and Facebook are leading sources of large datasets nowadays. Twitter’s data is one of the most reliable due to its privacy policy. Tweets have been used for sentiment analysis and to identify meaningful information within the dataset. Our study focused on the distance learning domain in Saudi Arabia by analyzing Arabic tweets about distance learning. This work proposes a model for analyzing people’s feedback using a Twitter dataset in the distance learning domain. The proposed model is based on the Apache Spark product to manage the large dataset. The proposed model uses the Twitter API to get the tweets as raw data. These tweets were stored in the Apache Spark server. A regex-based technique for preprocessing removed retweets, links, hashtags, English words and numbers, usernames, and emojis from the dataset. After that, a Logistic-based Regression model was trained on the pre-processed data. This Logistic Regression model, from the field of machine learning, was used to predict the sentiment inside the tweets. Finally, a Flask application was built for sentiment analysis of the Arabic tweets. The proposed model gives better results when compared to various applied techniques. The proposed model is evaluated on test data to calculate Accuracy, F1 Score, Precision, and Recall, obtaining scores of 91%, 90%, 90%, and 89%, respectively.https://peerj.com/articles/cs-1047.pdfSentiment analysisSocial mediaE-LearningTwitterApache SparkArabic language |
| spellingShingle | Jameel Almalki A machine learning-based approach for sentiment analysis on distance learning from Arabic Tweets PeerJ Computer Science Sentiment analysis Social media E-Learning Apache Spark Arabic language |
| title | A machine learning-based approach for sentiment analysis on distance learning from Arabic Tweets |
| title_full | A machine learning-based approach for sentiment analysis on distance learning from Arabic Tweets |
| title_fullStr | A machine learning-based approach for sentiment analysis on distance learning from Arabic Tweets |
| title_full_unstemmed | A machine learning-based approach for sentiment analysis on distance learning from Arabic Tweets |
| title_short | A machine learning-based approach for sentiment analysis on distance learning from Arabic Tweets |
| title_sort | machine learning based approach for sentiment analysis on distance learning from arabic tweets |
| topic | Sentiment analysis Social media E-Learning Apache Spark Arabic language |
| url | https://peerj.com/articles/cs-1047.pdf |
| work_keys_str_mv | AT jameelalmalki amachinelearningbasedapproachforsentimentanalysisondistancelearningfromarabictweets AT jameelalmalki machinelearningbasedapproachforsentimentanalysisondistancelearningfromarabictweets |