Location Sentiment Index Aggregation for Comparative Analysis
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Solution Overview
Problem
Current data mining techniques for sentiment analysis lack the ability to effectively compare and rank geographic locations based on diverse opinions and preferences, making it difficult for users to make informed decisions about travel or home purchases.
Innovation Solution
A computer-implemented method that computes a comparative indicator for geographic locations by aggregating sentiment analysis indices from various data sources, including weather, traffic, politics, facilities, and news, and displays these indicators on a local computing device, allowing users to visualize and interactively refine their search criteria.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional search query results are used for location information, then users can obtain basic location data, but users cannot obtain comprehensive and dynamic sentiment analysis from multiple sources
Solution Approach 1:
The patent combines sentiment analysis from multiple diverse sources including weather data, traffic data, political data, facility data, and news data into a unified comparative indicator for geographic locations. This merging of multiple data streams resolves the contradiction by providing comprehensive sentiment analysis while managing complexity through systematic integration.
Solution Approach 2:
The system creates a universal comparative indicator that serves multiple functions: it aggregates sentiment from various sources, provides location-based recommendations, and enables interactive exploration. This multi-functional approach addresses the information loss by creating a single comprehensive metric that captures diverse sentiment dimensions.
2Adaptability or versatility
If static search query results are provided, then users can obtain simple location data, but users cannot interactively refine search criteria or visualize results dynamically
Solution Approach 1:
The patent implements dynamic sentiment indicators that update based on user interactions and allow interactive refinement of search criteria. The system transitions from static search results to dynamic, adjustable sentiment analysis that adapts to user needs, resolving the contradiction between adaptability and complexity through intuitive interaction design.
Solution Approach 2:
The system introduces an intermediary visualization layer that mediates between complex multi-source sentiment data and user interpretation. This intermediary interface allows users to interactively refine searches and visualize results without exposing the underlying system complexity, resolving the contradiction between versatility and device complexity.
3Measurement precision
If comprehensive opinion data from multiple sources is aggregated, then users can obtain detailed location insights, but the system complexity and computational requirements increase
Solution Approach 1:
The patent segments the comprehensive sentiment analysis into distinct components: weather sentiment, traffic sentiment, political sentiment, facility sentiment, and news sentiment. Each component is analyzed separately and then aggregated into a comparative indicator. This segmentation resolves the contradiction by maintaining measurement precision through detailed analysis while managing computational complexity through modular processing.
Solution Approach 2:
The system transforms multiple diverse data parameters from different sources into a unified sentiment indicator parameter. By changing the parameter representation from raw diverse data to standardized sentiment scores, the system achieves precise measurement while reducing computational complexity through parameter normalization and aggregation.
Data Source
AI summary
Aspects of the present invention provide for methods that index geographic locations with comparative indicators that are determined from a sentiment analysis of opinion data, wherein the comparative indicators may include sums of different indices that are each determined from sentiment analysis of opinion data.