Channel-Aware Sentiment Analysis for Demographic Bias Reduction
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Solution Overview
Problem
Conventional sentiment analysis systems fail to adequately account for channel-specific biases in customer feedback, leading to inaccurate sentiment scoring due to demographic variations across different communication channels.
Innovation Solution
A system and method that adjusts sentiment scores by collecting user attributes on a per-channel basis, associating demographics, performing sentiment analysis to identify bias, determining a sentiment adjustment factor, and applying it to compensate for bias, thereby generating an adjusted sentiment score.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional sentiment analysis is applied without channel-specific adjustments, then the analysis process is simple and fast, but the sentiment scores are biased and inaccurate due to demographic variations across channels
Solution Approach 1:
The patent segments the sentiment analysis process by creating separate sentiment models for each communication channel (e.g., Twitter, Facebook, email). Each channel-specific model accounts for the unique demographic characteristics and communication styles of that platform, allowing for more accurate sentiment scoring without requiring a complete redesign of the entire system.
Solution Approach 2:
The patent modifies the sentiment analysis parameters by incorporating channel-specific demographic weights and adjustment factors. Instead of using a single universal sentiment model, the system adjusts key parameters such as sentiment thresholds, weightings, and interpretation criteria based on the specific channel being analyzed, thereby improving accuracy while maintaining a familiar analytical framework.
2Measurement precision
If channel-specific sentiment analysis is implemented, then sentiment bias is reduced and accuracy is improved, but computational resources and processing time increase
Solution Approach 1:
The patent performs preliminary actions by pre-processing and categorizing content according to its source channel before sentiment analysis. Demographic characteristics and channel-specific parameters are pre-calculated and stored, so that during actual sentiment analysis, the system can quickly retrieve and apply the appropriate parameters without performing complex calculations in real-time, thus maintaining efficiency while improving accuracy.
3Measurement precision
If demographic information is collected and analyzed on a per-channel basis, then bias identification is improved, but data collection complexity and privacy concerns increase
Solution Approach 1:
The patent implements a universal data collection framework that serves multiple functions: it gathers demographic information for bias detection, identifies channel characteristics, and enables sentiment analysis all through a single integrated process. This multi-functional approach reduces the need for separate data collection systems for each channel while improving bias detection accuracy through comprehensive demographic analysis.
Data Source
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AI summary
Provided is a system and method for adapting sentiment analysis to user profiles to reduce bias in customer or user generated content, specifically a system and method that discounts or adjusts bias in sentiment data based on the channel from which the content was received and/or the demographic of the user. The system includes a means to detect sentiment bias for any product, service, or company across multiple channels of customer data; a means to construct models to quantize bias by specific demographics and channels; and a means to adjust sentiment model output to reduce inflation by biased groups.