Qualitative and Artificial Intelligence-Based Sentiment Analysis of Turkish Tweets Related to Schizophrenia

Gül DİKEÇ, Volkan OBAN , Miraç Barış USTA
2023 34(3): 145-153
DOI: 10.5080/u26402
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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
Results: The study found that, of the total of 3406 schizophreniarelated
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.
Keywords: Natural language processing, machine learning,
schizophrenia, social stigma, social media