Abstract
Nowadays, social media outlets involve peoples’ opinions, reactions, and emotions. Sentiment analysis classifies the text from those sites into negative or positive polarity. Over the years, a multitude of researchers studied Arabic sentiment analysis but most of them focused on standard Arabic language. However, the Arabic dialects should have much concentration by researchers. Therefore, the main focus of this research is the sentiment analysis of the Iraqi Arabic dialect. The data sourced from Facebook platform (Posts and Comments), the most popular social media site in Iraq. Then, the data passed through several preprocessing steps and weighting methods. The processed data then passed into comparative experiments with six machine learning algorithms including Naïve Bays, Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), Random Forest, and K-Nearest Neighbor (KNN). The results indicated the highest accuracy achieved by Naïve Bays with 81.2%, followed by SVM and LR with 74%, while DT and Random Forest achieved accuracy 64% and 63%, respectively. The worst result was achieved by KNN algorithm of 57%.
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Mohammed, N.T., Mohammed, E.A., Hussein, H.H. (2023). Evaluating Various Classifiers for Iraqi Dialectic Sentiment Analysis. In: Kumar, R., Pattnaik, P.K., R. S. Tavares, J.M. (eds) Next Generation of Internet of Things. Lecture Notes in Networks and Systems, vol 445. Springer, Singapore. https://doi.org/10.1007/978-981-19-1412-6_6
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