AI Public Sentiment Management
Experimental Fantasy #03: AI Public Sentiment Management
LBS data and AI natural language processing technology can be essential in public sentiment management. The following is the LBS experimental idea initiated by MetaLab.
3-1 Public sentiment monitoring
Collect geotagged user social media information (comments, moods, behaviors, etc.) through LBS Chain Dapps and public information channels.Analyzing these LBS data lets you understand the general sentiment hotspots and events in a specific area. Combined with NLP and machine learning tech, automated public sentiment monitoring and analysis can be conducted to identify and track related topics and events quickly.
3-2 Sentiment analysis
Based on 3-1, AI technology can be applied to sentiment analysis to help judge public sentiment. Combined with LBS data, the content users post at specific locations can be analyzed and categorized into positive, negative, or neutral sentiments. This helps understand the public's emotional attitudes towards particular areas or events, guiding decision-making and response strategies to large-scale public events or informing infrastructure design.
3-3 Visualized Analysis
Based on the above, add more city information, and through visual means such as heat maps and scatter plots, the public sentiment data is displayed intuitively. This can help city managers to find better and solve problems and realize the idea of a smart city.
If you are interested in this direction, please email [no-reply@lbschain.org] with the subject "Experimental Fantasy #003." Let's build together.
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