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Going online has created more opportunities for newspapers to present breaking news in a timely manner. Concentrating in spreading more bad news increases the feeling of danger and depression in the society. Some authors believe on tendency of some media to be focused on sharing the bad events in life rather than the good ones because of the impact and the attraction over the audience is more significant. Sentiment analysis work has not been recognized, proposed, or documented on Arabic news because of the challenges that Arabic raises as a language including the different Arabs' dialects and its complex grammatical structure. With the emerging flow of news that cause a global panic and anxiety worldwide, this study focuses on the serious need to identify what effective role could machine learning classifiers have in the early detection process of the psychology impact on the readers by the daily news headlines. In this work, a dataset of news headlines were gathered from top popular online Arabic news sites and were annotated to seven emotional categories: anger, disgust, fear, happiness, neutral, sadness, and surprise. A convolutional neural network-based two-level approach was proposed for sentiment classification. The performance of the proposed approach was compared to six machine learning classifiers (zeroR, k-nearest neighbor, decision trees, naïve Bayes, random forest and Support vector machine) and showed better accuracy, precision, and recall.

Background. Healthcare is a challenging, yet so demanding sector that developing countries are paying more attention to recently. Statistics show that rural areas are expected to develop a high rate of heart diseases, which is a leading cause of sudden mortality, in the future. Thus, providing solutions that can assist rural people in detecting the cardiac risks early will be vital for uncovering and even preventing the long-term complications of cardiac diseases. Methodology. Mobile technology can be effectively utilized to limit the cardiac diseases' prevalence in rural Middle East. This paper proposes a smart mobile solution for early risk detection of hard coronary heart diseases that uses the Framingham scoring model. Results. Smart HeartCare+ mobile app estimates accurately coronary heart diseases' risk over 10 years based on clinical and nonclinical data and classifies the patient risk to low, moderate, or high. HeartCare+ also directs the patients to further treatment recommendations. Conclusion. This work attempts to investigate the effectiveness of the mobile technology in the early risk detection of coronary heart diseases. HeartCare+ app intensifies the communication channel between the lab workers and patients residing in rural areas and cardiologists and specialist residing in urban places.

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