Traffic accidents are the leading causes beyond death; it is the concern of most countries that strive for finding radical solutions to this problem. There are several methods used in the process of forecasting traffic accidents such as classification, assembly, association, etc. This paper surveyed the latest studies in the field of traffic accident prediction; the most important tools and algorithms were used in the prediction process such as Backpropagation Neural Networks and the decision tree. In addition, this paper proposed a model for predicting traffic accidents based on dataset obtained from the Directorate General of Traffic Statistics, Ibb, Yemen.

The dramatic increase in user comments describing their feelings about products, services, and events brings sentiment analysis to the forefront as a way to monitor public opinion about products and events. Feature selection is an important subtask of sentiment analysis, which aims to improve the performance of learning algorithms and reduce the dimensionality of a problem. Feature selection is an important subtask of sentiment analysis, as it can improve the performance of learning algorithms while reducing the dimensionality of a problem. Moreover, the high-dimensional feature spaces caused by the morphological richness of Arabic motivate further research in this area. In this paper, a hybrid filter-based and genetic feature selection algorithm is proposed using four machine learning algorithms, namely decision tree, Naive-Bayes, K-NN and meta-ensemble methods. The performance of the proposed algorithm is compared with the performance of baseline models. A wide range of experiments are conducted on two standard Arabic datasets. The experimental results clearly show that the improved methods outperform the other baseline models for Arabic sentiment analysis. The results show that the improved models outperform traditional approaches in terms of classification accuracy, with a 5% increase in the macro average of F1.