Sentiment Mapping via Spatial Path Association
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Solution Overview
Problem
Current social media sentiment analysis tools fail to identify the specific causes of consumer experiences at venues, such as positive or negative experiences, making it difficult for businesses to understand and address issues effectively.
Innovation Solution
A computer-implemented method that accesses social media data associated with profiles and venues, determines sentiment information, identifies paths through the venue, and associates sentiment with these paths to reveal trends, allowing businesses to improve customer experiences by adding support to negative areas, rerouting consumers, and redesigning layouts based on positive feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If social media sentiment analysis is performed without spatial mapping, then sentiment data can be collected, but the specific causes and locations of consumer experiences cannot be identified
Solution Approach 1:
The patent segments the venue into multiple spatial zones and segments sentiment data by associating it with specific paths and locations within the venue. This allows the system to maintain spatial context by dividing the overall sentiment analysis into location-specific components, thereby resolving the information loss while managing complexity through structured segmentation.
Solution Approach 2:
The patent introduces path information and location data as intermediary elements that connect social media sentiment expressions with physical venue spaces. These intermediaries enable the mapping between abstract sentiment data and concrete spatial locations, preserving spatial context without requiring direct complex integration of all system components.
2Measurement precision
If detailed path tracking is implemented for each profile, then specific venue experiences can be identified, but data processing complexity increases
Solution Approach 1:
The system segments path tracking into discrete segments associated with different venue zones and profiles. By dividing the continuous path data into manageable segments linked to specific locations, the system achieves precise sentiment location identification while reducing processing complexity through structured data organization.
Solution Approach 2:
The patent adds a spatial dimension to sentiment analysis by mapping sentiment data onto path segments and venue locations. This dimensional transformation allows precise location identification by projecting 1D path data into 2D spatial context, enhancing measurement precision while managing complexity through dimensional organization.
Data Source
AI summary
A computer-implemented method includes accessing social media data, wherein the social media data is associated with one or more profiles and corresponds to a venue. The computer-implemented method further includes determining sentiment information corresponding to each of the one or more profiles based on the social media data. The computer-implemented method further includes, for each of the one or more profiles: identifying a path through the venue, wherein the path represents at least one movement associated with the profile and associating the sentiment information with the path through the venue. The computer-implemented method further includes, responsive to associating the sentiment information with the path through the venue for each of the one or more profiles, identifying one or more trends. The computer-implemented method further includes presenting the one or more trends for review. A corresponding computer system and computer program product are also disclosed.