biostudies-literatureUnknown8Almalki JSocial 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, linPeerJ. Computer sciencee1047https://www.ebi.ac.uk/biostudies/studies/S-EPMC9454973biostudies-literatureA machine learning-based approach for sentiment analysis on distance learning from Arabic Tweets.PMC9454973Almalki JfalseA 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 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, lin2022-01-01T00:00:00Z20222025-04-18T18:24:53.526Z2025-04-07T06:03:03.658ZS-EPMC94549733609201110.7717/peerj-cs.1047