Sentiment Analysis System for Individual Subgroup Identification
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Solution Overview
Problem
Traditional methods for gauging customer sentiment, such as surveys, are resource-intensive and prone to inaccuracies due to participant overload and skewing, while existing sentiment analysis systems primarily focus on overall trends without detailed individual or subgroup analysis.
Innovation Solution
A system utilizing computer processors to analyze textual, audio, and video data from communications using statistical and linguistic sentiment analysis techniques to identify individuals or subgroups and provide real-time sentiment assessments, enabling detailed sentiment analysis beyond overall trends.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional survey methods are used to gauge customer sentiment, then sentiment data can be collected, but the process becomes resource-intensive and expensive
Solution Approach 1:
The patent replaces traditional mechanical survey methods with an automated computational system that uses natural language processing and machine learning algorithms to analyze communications data. This substitution eliminates manual survey administration and processing, significantly reducing resource requirements while maintaining or improving measurement precision through automated text and speech analysis.
Solution Approach 2:
The system creates computational models and digital representations of sentiment by analyzing existing communications data. Instead of requiring direct human participation in surveys, the system copies and processes existing communication patterns to derive sentiment information, thereby eliminating the need for additional resource-intensive survey activities.
2Measurement precision
If surveys are overused to maintain data quality, then more sentiment data can be gathered, but participant overload reduces accuracy and usefulness
Solution Approach 1:
The system enables sentiment analysis to occur automatically without requiring participant engagement or time investment. By analyzing existing communications that participants generate in their normal interactions, the system obtains sentiment data without imposing additional time burdens, thereby maintaining measurement precision while eliminating participant time loss.
Solution Approach 2:
The system performs sentiment analysis on communications data that already exists before any survey intervention is needed. By preprocessing and analyzing available communication records continuously, the system maintains up-to-date sentiment information without requiring participants to spend time on additional surveys.
3Reliability
If surveys are used frequently to maintain data relevance, then current sentiment can be tracked, but participant annoyance increases and responses may be skewed
Solution Approach 1:
The patent replaces human-administered surveys with an automated system that continuously monitors and analyzes communications data. This substitution eliminates the need for repeated survey interventions that cause participant annoyance, while maintaining reliable sentiment tracking through passive analysis of naturally occurring communications.
Solution Approach 2:
The system provides continuous sentiment analysis by continuously processing communications data in real-time or near-real-time. This continuous operation maintains data validity and relevance without requiring discrete survey events that would annoy participants, as the analysis occurs seamlessly in the background.
4Measurement precision
If existing sentiment analysis systems analyze newsfeeds and blogs, then overall trend information can be obtained, but detailed individual or subgroup analysis is not provided
Solution Approach 1:
The patent segments the sentiment analysis into multiple hierarchical levels: overall group sentiment, individual participant sentiment, and subgroup sentiment. By dividing the analysis into these distinct segments, the system simultaneously provides accurate overall trends while preserving and analyzing individual and subgroup-level details that would otherwise be lost in aggregate data.
Solution Approach 2:
The system adds dimensional granularity to sentiment analysis by introducing individual and subgroup dimensions alongside the overall group dimension. This multi-dimensional approach allows the system to maintain accurate overall trend measurement while simultaneously providing detailed information across additional analytical dimensions, thereby preventing information loss.
Data Source
AI summary
A system includes one or more computer processors that are configured to receive data relating to a composition of a target group, receive logged communications of the target group, extract textual information from the logged communications, analyze the textual information using statistical and linguistic sentiment analysis techniques, identify an individual or sub-group from the target group as a function of the analysis of the textual information, and display on a user interface or transmit to another processor the identified individual or sub-group of the target group and to display on the user interface or transmit to another processor a sentiment assessment of the identified individual or sub-group as a function of the statistical and linguistic sentiment analysis.