Saudi Cultural Missions Theses & Dissertations
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Item Restricted AUTOMATED DETECTION OF OFFENSIVE TEXTS BASED ON ENSEMBLE LEARNING AND HYBRID DEEP LEARNING TECHNIQUES(Florida Atlantic University, 2025-05) Alqahtani, Abdulkarim Faraj; Ilyas, MohammadThe impact of communication through social media is currently considered a significant social issue. This issue can lead to inappropriate behavior using social media, which is referred to as cyberbullying. The accessibility and freedom of expression afforded by social media platforms enable individuals to share their emotions and opinions, but it also leads to cyberbullying behavior. Automated systems are capable of efficiently identifying cyberbullying and performing sentiment analysis on social media platforms. In this dissertation, our focus is on enhancing a system to detect cyberbullying in various ways. Therefore, we apply natural language processing techniques utilizing artificial intelligence algorithms to identify offensive texts in various public datasets. The first approach leverages two deep learning models to classify a large-scale dataset, combining two techniques: data augmentation and the GloVe pre-trained word representation method to improve training performance. In addition, we utilized multi-classification algorithms on a cyberbullying dataset to identify six types of cyberbullying tweets. Our approach achieved high accuracy, particularly with TF-IDF (bigram) feature extraction, compared to previous experiments and traditional machine learning algorithms applied to the same dataset. We employed two ensemble machine learning methods with the TF-IDF feature extraction technique, which demonstrated superior classification performance. Moreover, we used four feature extraction methods, BoW, TF-IDF, Word2Vec, and GloVe, to determine which works best with the ensemble technique. Finally, we utilize a multi-channel convolutional neural network (CNN) enhanced with an attention mechanism and optimized using a focal loss function.10 0Item Restricted Embracing Emojis in Sarcasm Detection to Enhance Sentiment Analysis(University of Southampton, 2025) Alsabban, Malak Abdullah; Hall, Wendy; Weal, MarkPeople frequently share their ideas, concerns, and emotions on social networks, making sentiment analysis on social media increasingly important for understanding public opinion and user sentiment. Sentiment analysis provides an effective means of interpreting people's attitudes towards various topics, individuals, or ideas. This thesis introduces the creation of an Emoji Dictionary (ED) to harness the rich contextual information conveyed by emojis. It acts as a valuable resource for deciphering the emotional nuances embedded in textual content, contributing to a deeper understanding of sentiment. In addition, the research explores the complex domain of sarcasm detection by proposing a novel Sarcasm Detection Approach (SDA). This approach identifies sarcasm by analysing conflicts between textual content and the accompanying emojis. The thesis addresses key challenges in sentiment analysis by evaluating and comparing emoji dictionaries and sarcasm detection approaches to enhance sentiment classification. Extensive experimentation on diverse datasets rigorously assesses the effectiveness of these methods in improving sentiment analysis accuracy and sarcasm detection performance, particularly in emoji-rich datasets. The findings highlight the crucial role of emojis as contextual cues, underscoring their value in sentiment analysis and sarcasm detection tasks. The outcomes of this thesis aim to advance sentiment analysis methodologies by offering insights into preprocessing strategies, leveraging the expressive potential of emojis through the Emoji Dictionary (ED), and introducing the Sarcasm Detection Approach (SDA). The research demonstrates that integrating emojis through these tools substantially enhances both sentiment analysis and sarcasm detection. By utilizing these tools, the study not only improves model performance but also opens avenues for further exploration into the nuanced complexities of digital communication.19 0Item Restricted IMPROVING ASPECT-BASED SENTIMENT ANALYSIS THROUGH LARGE LANGUAGE MODELS(Florida state university, 2024) Alanazi, Sami; Liu, XiuwenAspect-Based Sentiment Analysis (ABSA) is a crucial task in Natural Language Processing (NLP) that seeks to extract sentiments associated with specific aspects within text data. While traditional sentiment analysis offers a broad view, ABSA provides a fine-grained approach by identifying sentiments tied to particular aspects, enabling deeper insights into user opinions across diverse domains. Despite improvements in NLP, accurately capturing aspect-specific sentiments, especially in complex and multi-aspect sentences, remains challenging due to the nuanced dependencies and variations in sentiment expression. Additionally, languages with limited annotated datasets, such as Arabic, present further obstacles in ABSA. This dissertation addresses these challenges by proposing methodologies that enhance ABSA capabilities through large language models and transformer architectures. Three primary approaches are developed and evaluated: First, aspect-specific sentiment classification using GPT-4 with prompt engineering to improve few-shot learning and in-context classification; second, triplet extraction utilizing an encoder-decoder framework based on the T5 model, designed to capture aspect-opinion-sentiment associations effectively; and lastly, Aspect-Aware Conditional BERT, an extension of AraBERT, incorporating a customized attention mechanism to dynamically adjust focus based on target aspects, particularly improving ABSA in multi-aspect Arabic text. Our experimental results demonstrate that these proposed methods outperform current baselines across multiple datasets, particularly in improving sentiment accuracy and aspect relevance. This research contributes new model architectures and techniques that enhance ABSA for high-resource and low-resource languages, offering a scalable solution adaptable to various domains.38 0Item 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.11 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.82 0