Granular Sentiment Analysis for Customer Feedback Segmentation
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Solution Overview
Problem
Existing manual processes for assessing customer sentiment from vast amounts of textual feedback are error-prone, time-consuming, and unable to scale, leading to missed feedback and patterns in customer complaints.
Innovation Solution
Implementing global segmenting and sentiment analysis based on granular opinion detection, which involves a system that automatically analyzes and segments large-scale free-form text in real-time, providing sentiment data at various granularities and allowing for the augmentation of entity taxonomies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processes are used to assess customer sentiment, then customer service representatives can identify feedback and patterns, but the process becomes error-prone, time-consuming, and unable to scale to large amounts of textual feedback
Solution Approach 1:
The patent replaces the mechanical manual review process with an automated computational system that uses natural language processing and machine learning algorithms to analyze customer feedback. This substitution eliminates human error and fatigue while processing vast amounts of textual data at scale, simultaneously improving both accuracy and productivity.
Solution Approach 2:
The system enables self-service sentiment analysis by automatically processing and analyzing customer feedback without requiring human intervention for each piece of data. The automated pipeline independently performs data retrieval, analysis, and pattern identification, freeing customer service representatives from manual review tasks while maintaining high precision.
2Loss of information
If manual processes are used to review large amounts of customer feedback, then some feedback and patterns may be identified, but the process is time-consuming and cannot scale to large volumes of data
Solution Approach 1:
The patent segments the large volume of customer feedback into manageable units and processes them through an automated pipeline. The system divides textual data into individual feedback items, processes each through analysis algorithms, and aggregates results. This segmentation enables complete coverage of all feedback without time constraints, as the automated system can process multiple segments simultaneously.
Solution Approach 2:
The automated sentiment analysis system operates continuously without interruption, processing customer feedback as it arrives without the breaks, fatigue, or sequential limitations of manual review. This continuous operation ensures no feedback is missed while dramatically reducing total analysis time compared to human reviewers who must work in shifts or sequentially.
3Reliability
If customer service representatives manually assess sentiment, then they can provide human judgment, but the process is error-prone and cannot keep pace with growing amounts of customer feedback
Solution Approach 1:
The patent replaces human judgment with automated computational analysis that eliminates human errors such as fatigue, bias, and inconsistency. The system uses standardized algorithms and machine learning models that apply the same criteria uniformly to all feedback, ensuring reliable and reproducible sentiment assessment across vast volumes of data that would be impossible for human reviewers to process consistently.
Data Source
AI summary
A global segmenting and analysis service of a provider network may receive documents (e.g., posts, product reviews) from different applications. The service may analyze the documents to identify target entities and sentiment. The service may generate different levels of sentiment data and store data into a segmented database. For example, the service may store within-document level sentiment, document-level sentiment, and multi-document level sentiment for a target entity. The service may also update the entity taxonomy automatically or with only a small number of sample documents. The client may query the service for the segmented sentiment data.