Over the past decade, smart city applications have gained significant attention in industrial informatics. However, little attention has been given to perceiving the emotions and perceptions of citizens who have a direct impact on smart city initiatives. In this article, we propose the use of publicly available abundant social media conversations that contain contextual information encompassing citizens' emotions and perceptions, which could be considered to provide the means to feel the “emotional pulse” of a city. We propose an automated AI-based observation framework to detect the emergence of public emotions and negativity in conversations. We evaluated the applicability of the framework using 29 928 social media conversations toward the much-debated topic of self-driving vehicles which will become increasingly relevant to smart cities. The patterns and transitions of citizens' collective emotions were modeled using the Natural Language Processing and Markov models while the negativity (toxicity) in conversations was evaluated using a deep learning based classifier. The framework could be adopted by industry leaders and government officials for smart observation of citizen opinions to improve security, communication, and policymaking.

中文翻译：

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了解公民_利用人工智能的智慧城市中的情感脉搏


在过去的十年中，智慧城市应用在工业信息学领域获得了广泛关注。然而，很少有人关注对智慧城市举措有直接影响的公民的情绪和看法。在本文中，我们建议使用公开的丰富社交媒体对话，其中包含包含公民情感和感知的上下文信息，这可以被视为提供感受城市“情感脉搏”的手段。我们提出了一种基于人工智能的自动化观察框架，以检测对话中公众情绪和消极情绪的出现。我们使用 29,928 个社交媒体对话来评估该框架的适用性，该对话涉及备受争议的自动驾驶汽车主题，该主题将与智慧城市越来越相关。使用自然语言处理和马尔可夫模型对公民集体情绪的模式和转变进行建模，同时使用基于深度学习的分类器评估对话中的消极性（毒性）。行业领导者和政府官员可以采用该框架来明智地观察公民意见，以改善安全、沟通和政策制定。