An ML-Based Classification Scheme for Analyzing the Social Network Reviews of Yemeni People

Abstract

The social network allows individuals to create public and semi-public web-based profiles to communicate with other users in the network and online interaction sources. Social media sites such as Facebook, Twitter, etc., are prime examples of the social network, which enable people to express their ideas, suggestions, views, and opinions about a particular product, service, political entity, and affairs. This research introduces an Machine Learning-based (ML-based) classification scheme for analyzing the social network reviews of Yemeni people using data mining techniques. A constructed dataset consisting of 2000 MSA and Yemeni dialects records used for training and testing purposes along with a test dataset consisting of 300 Modern Standard Arabic (MSA) and Yemeni dialects records used to demonstrate the capacity of our scheme. Four supervised machine learning algorithms were applied and a comparison was made of performance algorithms based on Accuracy, Recall, Precision and F-measure. The results show that the Support Vector Machine algorithm outperformed the others in terms of Accuracy on both training and testing datasets with 90.65% and 90.00, respectively. It is further noted that the accuracy of the selected algorithms was influenced by noisy and sarcastic opinions.