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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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Autor principal: Almalki, Jameel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9454973/
https://www.ncbi.nlm.nih.gov/pubmed/36092011
http://dx.doi.org/10.7717/peerj-cs.1047
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author Almalki, Jameel
author_facet Almalki, Jameel
author_sort Almalki, Jameel
collection PubMed
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.
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spelling pubmed-94549732022-09-09 A machine learning-based approach for sentiment analysis on distance learning from Arabic Tweets Almalki, Jameel PeerJ Comput Sci Human-Computer Interaction 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. PeerJ Inc. 2022-07-26 /pmc/articles/PMC9454973/ /pubmed/36092011 http://dx.doi.org/10.7717/peerj-cs.1047 Text en ©2022 Almalki https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Human-Computer Interaction
Almalki, Jameel
A machine learning-based approach for sentiment analysis on distance learning from Arabic Tweets
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 Human-Computer Interaction
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9454973/
https://www.ncbi.nlm.nih.gov/pubmed/36092011
http://dx.doi.org/10.7717/peerj-cs.1047
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