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

dc.authorid Dikec, Gul/0000-0002-7593-4014
dc.authorscopusid 57056407400
dc.authorscopusid 58082138500
dc.authorscopusid 56995772300
dc.authorwosid Usta, Mirac/L-7999-2017
dc.authorwosid Dikec, Gul/L-1623-2018
dc.contributor.author Dikeç, Gül
dc.contributor.author Oban, Volkan
dc.contributor.author Usta, Mirac Baris
dc.contributor.other Hemşirelik Bölümü
dc.date.accessioned 2025-01-11T13:04:29Z
dc.date.available 2025-01-11T13:04:29Z
dc.date.issued 2023
dc.department Fenerbahçe University en_US
dc.department-temp [Dikec, Gul] Fenerbahce Univ, Saglik Bilimleri Fak, Hemsirelik Bl, Istanbul, Turkiye; [Oban, Volkan] Istinye Univ, Guzel Sanatlar Tasarim & Mimarlik Fak, Dijital Oyun Tasarimi Bl, Istanbul, Turkiye; [Usta, Mirac Baris] Ondokuz Mayis Univ, Tip Fak, Cocuk Psikiyatrisi Anabilim Dali, Samsun, Turkiye en_US
dc.description Dikec, Gul/0000-0002-7593-4014 en_US
dc.description.abstract 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. en_US
dc.description.woscitationindex Social Science Citation Index
dc.identifier.citation 2
dc.identifier.doi 10.5080/u26402
dc.identifier.endpage 153 en_US
dc.identifier.issn 1300-2163
dc.identifier.issue 3 en_US
dc.identifier.pmid 37724640
dc.identifier.scopus 2-s2.0-85171900389
dc.identifier.scopusquality Q3
dc.identifier.startpage 145 en_US
dc.identifier.trdizinid 1255443
dc.identifier.uri https://doi.org/10.5080/u26402
dc.identifier.uri https://hdl.handle.net/20.500.14627/370
dc.identifier.volume 34 en_US
dc.identifier.wos WOS:001105680100002
dc.identifier.wosquality Q4
dc.language.iso tr en_US
dc.publisher Turkiye Sinir ve Ruh Sagligi dernegi en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 4
dc.subject Natural Language Processing en_US
dc.subject Machine Learning en_US
dc.subject Schizophrenia en_US
dc.subject Social Stigma en_US
dc.subject Social Media en_US
dc.title Qualitative and Artificial Intelligence-Based Sentiment Analysis of Turkish Tweets Related To Schizophrenia en_US
dc.type Article en_US
dc.wos.citedbyCount 3
dspace.entity.type Publication
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