Predictive Model Adjustment Using Sentiment and Relationship Data
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Solution Overview
Problem
Predictive modeling in finance struggles to accurately adjust predicted values for transactions, such as stock prices, due to lack of consideration for real-time sentiment and relationship dynamics between entities involved in the transaction.
Innovation Solution
A method that accesses transaction history data, real-time data, and media analysis to generate predictive models that estimate adjustment values for predicted transaction values, incorporating sentiment, value sensitivity, and relationship factors between entities, thereby improving prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional predictive modeling is used to predict transaction values, then the model is simple and fast to compute, but the prediction accuracy is insufficient because it does not account for sentiment and relationship dynamics
Solution Approach 1:
The predictive model is segmented into multiple independent components: a base predictive model for initial value estimation, a sentiment analysis module for processing media references, and a relationship dynamics module for analyzing entity interactions. Each component processes specific aspects of the transaction independently, then their outputs are combined to generate the final adjusted prediction, thereby improving accuracy without creating an intractably complex monolithic system
Solution Approach 2:
Sentiment scores and relationship strength metrics serve as intermediary variables that bridge the gap between raw data (media references, transaction history) and the final prediction adjustment. These intermediaries translate unstructured qualitative information into quantifiable factors that the predictive model can process, enabling accurate incorporation of sentiment and relationship dynamics while maintaining computational efficiency
2Measurement precision
If real-time sentiment analysis and relationship data are incorporated into the predictive model, then prediction accuracy improves, but the computational time and data processing requirements increase
Solution Approach 1:
Sentiment analysis and relationship strength calculations are performed in advance of the final prediction step. The system pre-processes media references to generate sentiment scores and pre-analyzes transaction history to establish relationship metrics before they are needed for the actual prediction adjustment, reducing computational time during critical prediction moments
Solution Approach 2:
The system applies different levels of analysis depth to different data sources based on their relative importance. High-impact factors such as recent sentiment shifts and strong relationship dynamics receive more intensive processing, while less influential factors undergo lighter processing, optimizing the balance between accuracy and computational time by allocating resources locally rather than uniformly
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for predictive modeling for adjusting initial values are disclosed. In one aspect, a method includes the actions of accessing transaction history data that indicates one or more transaction details associated with the transaction, a predicted value, and a final value. The actions further include determining a difference value between the predicted value and the final value. The actions further include generating a predictive model that is trained to estimate. The actions further include receiving one or more transaction details and a predicted value associated with a subsequently received transaction. The actions further include providing the one or more transaction details as input to the predictive model. The actions further include receiving an adjustment value to apply to the predicted value. The actions further include providing, for output, data indicating the adjustment value.