Sentiment Analysis for Service Tickets Using AI Segmentation
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Solution Overview
Problem
Conventional natural language processing techniques fail to identify user states of mind, such as distress or anger, in service tickets, leading to inadequate prioritization and failure to detect cascading failures in networked systems, thereby impacting user experience.
Innovation Solution
A method utilizing artificial intelligence to analyze natural language inputs by parsing, annotating, and mapping components with predetermined indicators, determining user sentiment, and associating traits to provide real-time sentiment analysis and trend analysis, enabling the identification and mapping of user states of mind.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional natural language processing techniques are used to automate support services, then the system can process service tickets, but it cannot identify user states of mind such as distress or anger
Solution Approach 1:
The system segments the service ticket text into individual words or phrases and analyzes each component separately using NLP techniques. This segmentation allows the system to identify sentiment-indicating words and map them to user states of mind, improving detection accuracy while managing complexity through modular processing
Solution Approach 2:
The patent introduces an intermediary layer that maps NLP-derived characteristics (such as sentiment scores, emotional indicators) to user states of mind. This intermediary transformation layer enables the system to infer psychological states from textual data without requiring direct observation, resolving the contradiction between accuracy and complexity
2Ease of operation
If the automated support system does not prioritize tickets based on user sentiment, then processing is straightforward, but user experience is drastically impacted when users are distressed
Solution Approach 1:
The system performs preliminary sentiment analysis on service tickets as they are received, before prioritization or routing decisions are made. By pre-processing and tagging tickets with user state indicators, the system enables subsequent automated prioritization of distressed users without adding manual intervention steps, maintaining operational simplicity while improving user experience reliability
Solution Approach 2:
The system implements feedback loops where sentiment analysis results from processed tickets are used to adjust prioritization rules and improve future processing. This feedback mechanism allows the system to learn from past interactions and automatically adapt prioritization strategies, ensuring reliable user experience management while keeping the core processing straightforward
3Loss of information
If conventional NLP techniques are used, then the system can analyze service tickets, but it cannot map similar tickets based on shared state of mind to identify cascading failures
Solution Approach 1:
The system merges sentiment analysis results with traditional NLP processing to create a unified view of service tickets that includes both technical content and emotional state information. This combination allows the system to group similar tickets by shared user states of mind, enabling efficient identification of cascading failures while maintaining complete information about user experiences
Solution Approach 2:
The patent creates a universal analysis framework that processes both technical ticket content and emotional indicators through the same NLP pipeline. This multi-functional approach allows the system to simultaneously perform traditional ticket analysis and sentiment-based pattern recognition, improving error detection efficiency without losing information about user states
Data Source
AI summary
A method for providing sentiment analysis of a natural language service request to automatically identify and map a user state of mind by utilizing artificial intelligence is disclosed. The method includes receiving, via a graphical user interface, a raw input, the raw input including a computer file corresponding to a natural language request; parsing the raw input into component parts; annotating, by using a model, each of the component parts with a predetermined indicator, the predetermined indicator corresponding to the user state of mind; mapping, by using the model, the component parts based on the predetermined indicator; compiling the mapped component parts into a structured input, and determining, by using the model, a quality that corresponds to the structured input.