WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14627/6
Browse
2 results
Search Results
Article Citation - WoS: 4Citation - Scopus: 5Qualitative and Artificial Intelligence-Based Sentiment Analysis of Turkish Tweets Related To Schizophrenia(Turkiye Sinir ve Ruh Sagligi dernegi, 2023) Dikec, Gul; Oban, Volkan; Usta, Mirac BarisObjective: The aim of this study was to qualitatively examine Turkish tweets about schizophrenia in respect of stigmatization and discrimination within a one-month period and to conduct emotional analysis using artificial intelligence applications. Method: Using the keyword 'schizophrenia,' Turkish tweets were gathered from the Python Tweepy application between December 19, 2020 and January 18, 2021. Features were extracted using the Bidirectional Encoder Representations from Transformers (BERT) method and artificial neural networks and tweets were classified as positive, neutral, or negative. Approximately 5% of the tweets were qualitatively analyzed, constituting those most frequently liked and retweeted. Results: The study found that, of the total of 3406 schizophrenia-related messages shared in Turkey over a period of one-month, 2996 were original, and were then retweeted a total of 1823 times, and liked by 25,413 people. It was determined that 63.4% of the tweets shared about schizophrenia contained negative emotions, 28.7% were neutral, and 7.71% expressed positive emotions. Within the scope of the qualitative analysis, 145 tweets were examined and classified under four main themes and two sub-themes; namely, news about violent patients, insult (insulting people in interpersonal relationships, insulting people in the news), mockery, and information. Conclusion: The results of this study showed that the Turkish tweets about schizophrenia, which were emotionally analyzed using artificial intelligence were found often to contain negative emotions. It was also seen that Twitter users used the term schizophrenia, not in a medical sense but to insult and make fun of individuals, frequently shared the news that patients were victims or perpetrators of violence, and the messages shared by professional branch organizations or mental health professionals were primarily for conveying information to the public.Article Citation - WoS: 4Citation - Scopus: 4Evaluation of Psychosocial Outcomes of Living Liver Donors in Liver Transplantation(Aves, 2022) Ordin, Yaprak Sarigol; Seren, Arzu Kader Harmanci; Karayurt, Ozgul; Kul, Gulay Aksu; Kilic, Murat; Bozoklar, Cemal Ata; Tokat, Yaman; Harmancı Seren, Arzu KaderBackground: The purpose of this study was to evaluate the risk factors affecting psychosocial outcomes of living liver donors after liver transplantations. Methods: This was a descriptive, cross-sectional study. The sample consisted of living liver donors followed by 2 liver transplantation centers in 2 private hospitals in 2 different provinces, between August 2017 and October 2019. All the living liver donors were contacted without a time frame after donation and all the participants were evaluated once. The Beck Depression Scale, SF-36, General Self-Efficacy Scale, and Perceived Available Support Scale were used to collect data. The t-test, Kruskal-Wallis test, Mann-Whitney U-test, and Pearson correlation analysis were used for data analysis. Results: The mean age of the patients was 34.31 +/- 8.22 years. There was a positive, weak correlation between age and physical function. Gender, marital status, financial status, and education levels significantly affected physical components, social function, vitality, depression, and self-efficacy scores. High depression levels negatively affected the physical component, self-efficacy, and social support scores of the living liver donors. High self-efficacy positively affected social support. Conclusion: The study revealed that gender, marital status, employment status, and education levels were associated with psychosocial outcomes. The financial status was the main factor affecting each psychosocial variable. Financial status needs to be assessed in detail before and after the operation.