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To increase the success in Covid 19 treatment, many drug suggestions are presented, and some clinical studies are shared in the literature. There have been some attempts to use some of these drugs in combination. However, using more than one drug together may cause serious side effects on patients. Therefore, detecting drug-drug interactions of the drugs used will be of great importance in the treatment of Covid 19. In this study, the interactions of 8 drugs used for Covid 19 treatment with 645 different drugs and possible side effects estimates have been produced using Graph Convolutional Networks. As a result of the experiments, it has been found that the hematopoietic system and the cardiovascular system are exposed to more side effects than other organs. Among the focused drugs, Heparin and Atazanavir appear to cause more adverse reactions than other drugs. In addition, as it is known that some of these 8 drugs are used together in Covid-19 treatment, the side effects caused by using these drugs together are shared. With the experimental results obtained, it is aimed to facilitate the selection of the drugs and increase the success of Covid 19 treatment according to the targeted patient.

Nowadays, social media and online sharing sites are frequently used to share thoughts about daily events. Thanks to the posts made by internet users on these platforms, first, quite big data is generated to interpret the agenda. More than 10,000 comments of more than 5000 users made about COVID-19 from online websites between 15 March and 15 May were collected in this study. Then, emotional analysis on these comments was carried out with BERT, GRU, LSTM and TF-IDF methods. The changes in the amount of user comments and the emotions reflected by the comments have been associated with the actual events of these dates. It has been determined which types of events affect users more. In addition, the emotional response changes of the users to the official COVID-19 statistics were measured and the peak points of the emotional changes were determined. Finally, the emotion classification methods applied were evaluated by user questionnaires and their successes were determined according to F-Measure.

To increase the success in Covid 19 treatment, many drug suggestions are presented, and some clinical studies are shared in the literature. There have been some attempts to use some of these drugs in combination. However, using more than one drug together may cause serious side effects on patients. Therefore, detecting drug-drug interactions of the drugs used will be of great importance in the treatment of Covid 19. In this study, the interactions of 8 drugs used for Covid 19 treatment with 645 different drugs and possible side effects estimates have been produced using Graph Convolutional Networks. Organ systems and diseases in which these 8 drugs cause the most negative effects have been identified. In addition, as it is known that some of these 8 drugs are used together in Covid-19 treatment, the side effects caused by using these drugs together are shared. With the experimental results obtained, it is aimed to facilitate the selection of the drugs and increase the success of Covid 19 treatment according to the targeted patient.

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In this research, the classification results of different Machine Learning Algorithms were compared on the validated TREMO data set used in the field of emotion extraction from Turkish texts. Emotion analysis was considered as text classification problem and four different machine algorithms, Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forest (RF) and K-Nearest Neighbor (KNN) have been investigated. The categories provided by the data set, which are happiness, fear, anger, sadness, disgust and surprise, were used as emotion categories. In the preprocessing phase, stemming process was performed using the truncate at five (F5) method. After stemming process, the data set was modeled using the Vector Space Model. After that, the first 500 words for each emotion in the data set were identified by the Mutual Information (MI) formula. The comparison of classification results was based on accuracy metric. According to experimental study results, the ANN classifier was performed best, and SVM, RF and KNN performed, in descending order.

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