Predictive Model Correction via Social Media Feature Integration
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Solution Overview
Problem
Existing predictive models for products and services struggle to accurately account for real-time customer opinions and sentiments from social media, which can significantly impact buying behavior and sales, due to their complexity and difficulty in modification.
Innovation Solution
The method involves analyzing social media data to identify predictive features, compute correlation measures, and modify existing predictive model outputs by incorporating the most relevant social media features, such as keyword trends and sentiment fluctuations, without requiring specific details of the existing model, thereby enhancing prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing predictive models are made more complex to account for social media data, then prediction accuracy improves, but model complexity and difficulty of modification increase
Solution Approach 1:
The patent segments the predictive modeling task into two independent parts: (1) an existing predictive model that processes traditional business data, and (2) a separate social media analysis system that processes social media data. The outputs of both systems are then combined through meta-analysis. This segmentation allows each component to remain simple while achieving high overall accuracy through their integration.
Solution Approach 2:
The patent introduces meta-analysis as an intermediary layer that combines the outputs of the existing predictive model and the social media analysis system. This intermediary component synthesizes information from both sources without requiring modification of the original models, thereby maintaining their simplicity while improving overall prediction accuracy.
2Measurement precision
If existing predictive models are modified to incorporate social media features, then prediction accuracy improves, but ease of operation deteriorates due to difficulty in modification
Solution Approach 1:
The patent extracts the social media analysis functionality from the existing predictive model and implements it as a separate, independent system. The social media data processing, feature extraction, and correlation analysis are performed outside the original model boundaries. This extraction allows the existing model to remain unchanged and easy to operate while still benefiting from social media insights through the combined meta-analysis output.
Solution Approach 2:
The patent creates a parallel copy of the predictive modeling process specifically for social media data. Instead of modifying the original model, a separate analysis pipeline is constructed that mirrors the predictive modeling approach but operates on social media features. The results from this copied process are then integrated with the original model output, maintaining operational simplicity while improving accuracy.
3Reliability
If social media data analysis is performed in real-time to capture customer sentiments, then prediction relevance improves, but processing time and computational resources increase
Solution Approach 1:
The patent performs preliminary analysis of social media data by pre-computing key features such as sentiment scores, keyword frequencies, and trend indicators. These pre-processed features are stored and ready for rapid integration with predictive model outputs when needed. This preliminary action reduces the computational burden during real-time prediction, maintaining relevance while minimizing processing time.
Solution Approach 2:
The patent focuses on analyzing only the most relevant social media features rather than processing all available data. By identifying and prioritizing key predictive features (such as sentiment fluctuations and keyword trends) that have the strongest correlation with sales predictions, the system achieves high prediction relevance with reduced computational effort and faster processing times.
Data Source
AI summary
Methods, systems, and computer program products for correcting existing predictive model outputs with social media features over multiple time scales are provided herein. A method includes examining multiple items of user content derived from one or more social media sources during a given time period to identify one or more items of user content pertaining to a target entity; analyzing the items of user content pertaining to the target entity to determine one or more predictive features in the items of user content related to a target variable associated with the target entity; computing a correlation measure between each of the predictive features and the target variable; and modifying an output of an existing predictive model associated with the target variable via incorporation of the predictive feature with the highest correlation measure into the output of the existing predictive model to generate an updated predictive output.