利用人工智能進行情感分析，以有效管理智慧城市環境中糖尿病患者的健康危機 Sentiment Analysis Utilizing Artificial Intelligence for Effe ctive Health Crisis Management in Diabetics in Smart Urban Environments ——《應用科學與技術趨勢雜誌》第7卷，第1期，2026年—— <Journal of Applied Science and Technology Trends> Volume 7, Issue 1, January 2026 【摘要】利用人工智能進行情感分析為智慧城市環境中糖尿病患者的健康危機管理提供了一種變革性的方法。本研究提出了一種基於人工智能的實用解決方案，該方案可集成到現有的智慧城市基礎設施中，以支持對糖尿病患者的實時健康危機干預。利用人工智能進行糖尿病患者健康危機管理的情感分析面臨的挑戰包括：需要高質量、多樣化的數據來準確捕捉情感，以及在智慧城市環境中處理敏感健康信息可能存在的隱私問題。本研究旨在利用人工智能進行情感分析，以增強智慧城市環境中糖尿病患者的健康危機管理。由於文本數據來源通常包含無關信息、垃圾信息和異常值等噪聲，因此在預處理階段採用自適應中值濾波技術（AMFT）來降低情感分析中的噪聲。AMFT用於降噪，循環神經網絡（RNN）用於時間情感分析，以及人工智能驅動的優化相結合，為健康危機預測系統引入了一種新穎且技術先進的方法。循環神經網絡 (RNN) 模型因其能夠處理序列數據並捕捉時間依賴性，在情感分析方面表現出色，尤其是在智慧城市環境中糖尿病患者的健康危機管理方面。人工智能驅動優化 (AIDO) 可以自動調整 RNN 中情感分析模型的超參數，從而提升模型性能，確保其準確性和高效性。該人工智能驅動的情感分析系統優於傳統的監測方法，例如基於規則的詞典和基於關鍵詞頻率的方法（使用Python實現），其準確率達到0.92，精確率達到 0.90，召回率達到 0.93。該系統體現了對應用科學和技術創新的重視，展示了一個可擴展的智能健康監測框架，可部署於智慧城市和城市衛生系統中。未來，利用人工智能進行情感分析的進一步發展，有望通過整合更多樣化的數據源和自適應學習算法，增強對糖尿病患者健康危機的實時監測和預測。 【關鍵詞】情感分析、糖尿病患者、自適應中值濾波、人工智能驅動的優化、智慧城市環境、危機管理 [Abstract] Sentiment analysis utilizing artificial intelligence offers a transformative approach to managing health crises among diabetics in smart urban environments. This research proposes a practical AI-based solution that can be integrated into existing smart urban infrastructure to support real-time health crisis interventions for diabetic patients. Challenges in sentiment analysis for health crisis management in diabetics using AI include the need for high-quality, diverse data to accurately capture sentiment and the potential for privacy issues with sensitive health information in smart urban environments. The objective of this study is to leverage sentiment analysis utilizing artificial intelligence to enhance health crisis management for diabetics within smart urban environments. Adaptive Median Filtering Technique (AMFT) is used in pre-processing to reduce noise in sentiment analysis, as textual data from sources often contains noise such as irrelevant information, spam, and outliers. The combination of AMFT for noise reduction, RNNs for temporal sentiment analysis, and AI-driven optimization introduces a novel, technologically advanced approach to health crisis prediction systems. Recurrent Neural Network (RNN) models are highly effective for sentiment analysis, especially in the health crisis management of diabetics within smart urban environments, due to their ability to process sequential data and capture temporal dependencies. AI-driven optimization (AIDO) can automatically tune hyperparameters of sentiment analysis models in RNNs to improve performance, ensuring the models are both accurate and efficient. The AI-driven sentiment analysis system outperforms traditional monitoring methods, such as rule-based lexicons and keyword frequency-based approaches implemented in Python, achieving an accuracy of 0.92, a precision of 0.90, and a recall of 0.93.The proposed system reflects the focus on applied science and technological innovations by demonstrating a scalable, intelligent health monitoring framework that can be deployed in smart cities and urban health systems. Future advancements in sentiment analysis using artificial intelligence could enhance real-time monitoring and prediction of health crises in diabetics, integrating more diverse data sources and adaptive learning algorithms. [Key words] Sentiment Analysis, Diabetics, Adaptive Median Filtering, AI-driven optimization, Smart Urban Environments, Crisis Management 論文原文：BH Krishna Mohan, Mong-Fong Horng, Siva Shankar S, Chun-Chih Lo (2026). Sentiment Analysis Utilizing Artificial Intelligence for Effective Health Crisis Management in Diabetics in Smart Urban Environments. Journal of Applied Science and Technology Trends, Volume 7, Issue 1, Pages: 01-15. January 2026. https://doi.org/10.38094/jastt71576 （翻譯兼責任編輯：MARY） （需要英文原文的朋友，請聯繫微信：millerdeng95或iacmsp）
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