Browsing by Author "ALKETBI, SULTAN"

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    Arabic Sentiment Analysis for Gulf Opinion Leaders using a Deep Learning Approach Case Study: Covid-19-22
    (The British University in Dubai (BUiD), 2023-07) ALKETBI, SULTAN
    The COVID-19 pandemic has had a profound impact on global health and has affected various populations worldwide. In the Arab world, social media has emerged as a critical platform for expressing opinions, sharing information, and disseminating news related to COVID-19. However, the proliferation of false information and the spread of fear and panic on social media have created a significant problem. This study aims to investigate how Arab populations, including both opinion leaders and the general public, have responded to the COVID-19 pandemic on Twitter. The research focuses on analysing sentiment and developing a deep learning model to detect real news associated with the pandemic in Arabic text. By gathering and analyzing data from Gulf countries, the study provides insights into the sentiments expressed and contributes to understanding how opinion leaders and the general public engage with COVID-19 on Twitter. Additionally, the study evaluates the efficacy of the deep learning model in combating misinformation and highlights the significance of sentiment analysis and news detection in the Arabic language. Data collection was conducted using Twitter's API, focusing on Arabic tweets from Gulf opinion leaders, utilizing specific keywords, hashtags, and user accounts related to COVID-19. The testing phase involved collecting 100,000 tweets from January to June 2022, with an emphasis on quality and relevance, including opinion leaders with significant follower counts and those recognized for their expertise or influence in the field. Overall, this research contributes to understanding the response to COVID-19 on Twitter and provides valuable insights into sentiment analysis and the detection of real news in Arabic text.