Saudi Cultural Missions Theses & Dissertations
Permanent URI for this communityhttps://drepo.sdl.edu.sa/handle/20.500.14154/10
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Item Restricted IS THE METAVERSEFAILING? ANALYSINGSENTIMENTS TOWARDSTHEMETAVERSE(The University of Manchester, 2024) Alharbi, Manal Dowaihi; Batista-navarro, RizaThis dissertation investigates Aspect-Based Sentiment Analysis (ABSA) within the context of the Metaverse to better understand opinions on this emerging digital environment, particularly from a news perspective. The Metaverse, a virtual space where users can engage in various experiences, has attracted both positive and negative opinions, making it crucial to explore these sentiments to gain insights into public perspectives. A novel dataset of news articles related to the Metaverse was created, and Target Aspect-Sentiment Detection (TASD) models were applied to analyze sentiments ex pressed toward various aspects of the Metaverse, such as device performance and user privacy. A key contribution of this research is the evaluation of the TASD architecture, TAS-BERT, and its enhanced version, Advanced TAS-BERT (ATAS-BERT), which performs each task separately, on two datasets: the newly created Metaverse dataset and the SemEval15 Restaurant dataset. They were tested with different Transformer based models, including BERT, DeBERTa, RoBERTa, and ALBERT, to assess performance, particularly in cases where the target is implicit. The findings demonstrate the ability of advanced Transformer models to handle complex tasks, even when the target is implicit. ALBERT performed well on the simpler Metaverse dataset, while DeBERTa and RoBERTa showed superior performance on both datasets. This dissertation also suggests several areas for improvement in future research, such as processing paragraphs instead of individual sentences, utilizing Meta AI models for dataset annotation to enhance accuracy, and designing architectures specifically for models like DeBERTa, RoBERTa, and ALBERT, rather than relying on architectures originally designed for BERT, to improve performance. Additionally, incorporating enriched context representations, such as Part-of-Speech tags, could further enhance model performance.9 0Item Restricted NHS Communication Strategies through Twitter ( X ) during the Covid-19 Pandemic in the UK(Swansea University, 2023-11-28) Alrashid, Saad; Wu, YanThis study evaluates Twitter engagement and crisis communication strategies, particularly emphasising the National Health Service (NHS) and the Covid-19 epidemic. The study intends to shed light on the NHSUK Twitter account as a crucial medium for spreading lockdown and government announcements during Covid-19. The research opens by noting the exceptional difficulties that public health organisations globally had during the epidemic and the critical role that NHSUK played in informing the public. It examines how the account handled different pandemic phases, including preventive measures, lockdowns, and reopening efforts. The study prepares the ground for analysing user replies and sentiments on Twitter by providing a theoretical framework encompassing crisis communication theories, theoretical approaches to social media analysis, and Risk Communication and Community Engagement (RCCE) model. It digs into the methodology, which includes data processing, sentiment analysis, and Twitter data acquisition. Key findings highlight the wide range of user responses, from thanks for timely information to discussions on governmental decisions and the lack of two-way communication. The research strongly emphasises the value of clear and prompt communication during crises, the necessity of combating false information and the difficulties of balancing optimism and caution while engaging the public. The research report highlights the importance of this study in boosting crisis communication strategy, raising public confidence in sources of health information, and teaching essential lessons for handling future health emergencies. It fills a vacuum in the literature addressing the precise effect of crisis communication on Twitter during medical emergencies, providing advice for practitioners and academics.80 0