Pattern-Level Sentiment Prediction Using Binary Indicators
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Solution Overview
Problem
Existing methods, such as gradient boosting, struggle to effectively predict user sentiment from time series user interaction data due to the loss of valuable information when treating ordered touchpoints as either discrete or continuous variables, making it difficult to analyze time-based user interaction data.
Innovation Solution
The method involves identifying patterns in user interaction data, generating pattern-level sentiments, and using a gradient boosting model trained with binary indicators to predict user sentiment in subsets of interaction data, allowing for the preservation of time series relationships without information loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If time series user interaction data is treated as discrete or continuous variables for gradient boosting analysis, then the data can be processed by standard machine learning models, but valuable time-based information is lost
Solution Approach 1:
The patent segments time series interaction data into discrete time bins or intervals, creating binary indicators for each bin that preserve the temporal structure. This segmentation allows the data to be processed by gradient boosting models while maintaining time-based relationships through the binary indicator representation of interactions within specific time windows.
Solution Approach 2:
The patent introduces binary indicators as an intermediary representation layer between the raw time series data and the gradient boosting model. These binary indicators serve as a mediator that encodes temporal information in a format compatible with tree-based models, allowing time series patterns to be preserved without requiring the model to directly process continuous time values.
2Ease of manufacture
If user interaction data is analyzed without preserving time series relationships, then standard analysis methods can be applied, but accurate sentiment prediction becomes difficult
Solution Approach 1:
The patent performs preliminary processing of time series data by creating binary indicators that encode temporal relationships before feeding the data to the gradient boosting model. This preliminary action of transforming time series data into binary temporal indicators enables standard analysis methods to be applied while preserving the time-based patterns necessary for accurate sentiment prediction.
3Loss of information
If pattern-level sentiment is used to aggregate user interactions, then time series relationships are preserved, but the complexity of the analysis increases
Solution Approach 1:
The patent segments the complex time series analysis task into pattern-level sentiment extraction at different time scales (e.g., recent interactions vs. historical interactions). This segmentation breaks down the complex temporal analysis into manageable pattern recognition tasks that can be processed by the gradient boosting model while preserving time series relationships through the hierarchical pattern structure.
Data Source
AI summary
Based on the interaction data and response data, an interaction monitoring platform may determine a first known sentiment and a second known sentiment, identify a first pattern and a second pattern in the interaction data, and generate a first pattern-level sentiment and a second pattern-level sentiment based on the known sentiments and the identified patterns. A binary indicator may indicate which identified patterns are exhibited in a subset of the interaction data. The platform may train a gradient boosting model using known sentiment as a target variable and using binary indicators and pattern-level sentiments as input data. The platform may predict a sentiment corresponding to a subset of interaction data with unknown sentiment that exhibits one or more of the first pattern or the second pattern based on a binary indicator and the trained gradient boosting model.