Metropolis AI: Citizen Sentiment Analyzer

Metropolis AI draws inspiration from the societal divides depicted in Fritz Lang's 'Metropolis' and the potential for systemic instability explored in Dan Simmons' 'Hyperion'. The project is a citizen sentiment analysis tool designed for local governments and municipalities. It leverages readily available public data (e.g., social media posts with geotags, municipal complaint portals, open forum comments, local news articles) to identify and analyze prevailing sentiments within specific districts or neighborhoods. The 'AI Workflow for Companies' scraper concept is adapted to gather and process this public data. The system uses NLP models to detect emotional tones (positive, negative, neutral, anger, fear, joy, sadness) associated with specific topics or concerns. These sentiments are then aggregated and visualized on a map-based interface, allowing city planners and officials to quickly identify areas with high levels of negative sentiment or potential social unrest. Further analysis can correlate these sentiments with demographic data or service provision levels to pinpoint areas requiring immediate attention or resource allocation. The low-cost aspect is achieved by using open-source NLP libraries and readily available cloud services for processing and storage. The earning potential lies in offering this tool as a SaaS subscription to local governments, with tiered pricing based on the size and complexity of the municipality. The niche is focusing specifically on local government needs and tailoring the sentiment analysis to issues relevant to urban planning and public services (e.g., traffic congestion, crime rates, waste management). The tool offers proactive insight, preventing potential problems before they escalate, rather than simply reacting to crises after they occur.