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

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dc.authoridDikec, Gul/0000-0002-7593-4014;
dc.authorwosidDikec, Gul/L-1623-2018
dc.authorwosidUsta, Mirac Baris/L-7999-2017
dc.contributor.authorDikec, Gul
dc.contributor.authorOban, Volkan
dc.contributor.authorUsta, Mirac Baris
dc.date.accessioned2024-05-19T14:45:59Z
dc.date.available2024-05-19T14:45:59Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractObjective: 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.en_US
dc.identifier.doi10.5080/u26402
dc.identifier.endpage153en_US
dc.identifier.issn1300-2163
dc.identifier.issue3en_US
dc.identifier.pmid37724640en_US
dc.identifier.scopus2-s2.0-85171900389en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage145en_US
dc.identifier.urihttps://doi.org10.5080/u26402
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5409
dc.identifier.volume34en_US
dc.identifier.wosWOS:001105680100002en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isotren_US
dc.publisherTurkiye Sinir Ve Ruh Sagligi Dernegien_US
dc.relation.ispartofTurk Psikiyatri Dergisien_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectNatural Language Processingen_US
dc.subjectMachine Learningen_US
dc.subjectSchizophreniaen_US
dc.subjectSocial Stigmaen_US
dc.subjectSocial Mediaen_US
dc.titleQualitative and Artificial Intelligence-Based Sentiment Analysis of Turkish Tweets Related to Schizophreniaen_US
dc.typeArticleen_US

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