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    Qualitative 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 Baris
    Objective: 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.
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    A sentiment analysis of Turkish tweets shared in nursing week during the pandemic
    (2022) Oban, Volkan; Doğan, Muzaffer Berna; Dikeç, Gül
    Aim: This study aimed to conduct an artificial intelligence-based sentiment analysis of Turkish tweets about nursing during the nursing week during the COVID-19 pandemic. Method: This is a retrospective descriptive survey. Between May 4 and May 19, 2021, Turkish tweets were analyzed using the Python library Tweepy. The search terms “nurse, nursing, and nursing week” were used to analyzed tweets for their positivity, neutrality, or negativity. Results: The analysis of 24,944 tweets revealed that tweets frequently express neutral emotions. The negative tweets frequently discussed issues such as societal gender perception, professionalism, burnout during the pandemic, salaries, inadequate nursing workforce, inequalities, violence against healthcare professionals, and the deaths of nurses. Conclusions: Social media applications can be recommended as important tools for raising awareness of the nursing profession identity, professionalism, visibility, and the perception of society towards nursing, nursing problems, and recommendations for solutions.