基于定性和人工智能的土耳其精神分裂症相关推文情绪分析。

Gül Dikeç, Volkan Oban, Miraç Barış Usta
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引用次数: 1

摘要

目的:本研究的目的是在一个月内对土耳其关于精神分裂症的推文在污名化和歧视方面进行定性检查,并使用人工智能应用程序进行情绪分析。方法:使用关键词“精神分裂症”,从Python Tweepy应用程序收集2020年12月19日至2021年1月18日期间的土耳其推文。使用来自变压器的双向编码器表示(BERT)方法提取特征,并将人工神经网络和推文分类为正、中性或负。大约5%的推文进行了定性分析,构成了最常被点赞和转发的推文。结果:研究发现,在土耳其一个月内分享的3406条精神分裂症相关信息中,2996条是原创的,随后被转发了1823次,有25413人点赞。经确定,在分享的关于精神分裂症的推文中,63.4%包含负面情绪,28.7%为中性,7.71%表达积极情绪。在定性分析的范围内,对145条推文进行了审查,并将其分为四个主要主题和两个子主题;即关于暴力患者的新闻、侮辱(在人际关系中侮辱人,在新闻中侮辱人)、嘲笑和信息。结论:本研究结果表明,使用人工智能进行情感分析的土耳其关于精神分裂症的推文往往包含负面情绪。还可以看到,推特用户使用精神分裂症一词,不是医学意义上的,而是侮辱和取笑个人,经常分享患者是暴力受害者或施暴者的消息,专业分支组织或心理健康专业人员分享的信息主要是为了向公众传达信息。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Qualitative and Artificial Intelligence-Based Sentiment Analysis of Turkish Tweets Related to Schizophrenia.

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 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.

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