Aspect-Based Sentiment Analysis of Arabic Tweets in the Education Sector Using a Hybrid Feature Selection Method

Sentiment analysis can be applied in many domains given the abundance of views in social networks, including the education sector, reflecting how cultures and nations grow and develop. In this context, aspect-based sentiment analysis with its two main tasks; aspect detection and aspect-opinion classification, might provide an accurate picture of many educational institutions ' strengths and weaknesses. In this research, a real-world Twitter dataset was collected, containing approximately 7,934 Arabic tweets related to Qassim University in Saudi Arabia. In the text classification task, the high dimensionality problem is usually faced, and the feature selection methods contribute to tackling this problem. Accordingly, the purpose of the experimental study is to investigate the effectiveness of using a hybrid feature selection method in improving the results of aspect-based sentiment analysis by reducing the number of features. The proposed hybrid feature method consists of a one-way analysis of variance to examine the relationship between each feature and classes, and the ridge regression that calculates the importance of features together during the learning phase. Several experiments were conducted to study the effects of the proposed feature selection method on improving the Support Vector Machine classifier 's performance. The experimental results demonstrate that the hybrid method successfully enhances the classifier's performance in both subtasks.