Application of Support Vector Machine for Arabic Sentiment Classification Using Twitter-Based Dataset

Abstract

Sentiment classification is the process of classifying emotions and opinions in texts. In this study, the problem of Arabic sentiment analysis was addressed. A support vector machine (SVM) model was proposed to classify opinions in Arabic micro-texts as being positive or negative. To evaluate the performance of the SVM model, a dataset was built from tweets discussing several social issues in Saudi Arabia. These issues include changes that were implemented by the country as part of a newly established vision, known as Saudi Arabia Vision 2030. The constructed dataset was manually annotated according to the sentiment conveyed in the text. To achieve the best sentiment classification accuracy, several procedures were implemented within the proposed framework including light stemming, feature extraction (Ngrams, emoji and tweet-topic features), parameter optimisation and feature-set reduction. The experimental results revealed excellent outcomes. An accuracy of 89.83% was achieved using the proposed SVM model.