Abstract:
Urban environments are dynamic and multifaceted, with diverse factors influencing the sentiment and emotions of their inhabitants. This paper presents a comprehensive model for real time urban emotion and sentiment analysis, utilizing data from social media, news articles, video feeds, and sensors. By employing advanced natural language processing and computer vision techniques, this model aims to provide policymakers and urban planners with actionable insights to enhance public engagement, inform urban design, and create responsive, inclusive environments. With the digitalization process accelerating globally, nearly everyone uses smartphones and social media. People increasingly read news articles online rather than using printed materials. For safety, many individuals install CCTV cameras in their shops and homes, especially in densely populated areas. Additionally, there is a growing awareness and attention to environmental indices compared to past years. This research aims to incorporate these factors social media usage, online news consumption, widespread CCTV installations, and increased environmental awareness into an integrated model. By analyzing emotions and sentiments, the model seeks to determine whether a location is suitable for people to spend their time based on collective emotional responses.
