Sentiment Analysis Using Log-Generated Synthetic Text
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Solution Overview
Problem
Natural language processing for sentiment analysis faces challenges when text data is lacking or unavailable, particularly in domains where user feedback is limited or unreliable, such as software systems, where surveys may lack domain understanding and tickets provide insufficient text data.
Innovation Solution
A method that utilizes log data from user interactions with software systems to generate synthesized text through a text generation model, which is then processed by a sentiment prediction model trained with similarity-based labels to predict user sentiment, reducing the need for frequent user feedback and addressing data and label issues by using system logs and historical ticket data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If feedback surveys are used to collect user sentiment, then user feedback can be gathered, but the surveys have insufficient domain understanding and may be neglected or biased
Solution Approach 1:
The patent introduces an intermediary system that automatically generates synthetic feedback based on system logs and NLP analysis. This intermediary bridges the gap between raw system data and meaningful user sentiment, eliminating the need for direct user surveys while maintaining feedback quality and reliability through automated domain-specific analysis.
Solution Approach 2:
The system performs self-service by automatically analyzing its own operational logs to generate synthetic user feedback. Instead of externally surveying users, the system uses its internal data (logs, errors, usage patterns) to infer user sentiment, making the feedback collection process autonomous and eliminating survey-related biases.
2Quantity of substance
If feedback surveys are frequently requested from users, then more user feedback can be collected, but user irritation and bias increase
Solution Approach 1:
The system generates feedback autonomously from its own logs without repeatedly contacting users. This self-service approach provides continuous feedback data collection while completely eliminating user irritation caused by frequent survey requests, as the system analyzes its operational data independently.
Solution Approach 2:
An intermediary NLP processing layer transforms system logs into synthetic feedback representations. This intermediary converts raw operational data into meaningful sentiment information without requiring direct user interaction, maintaining feedback quantity while avoiding user irritation.
3Loss of information
If tickets are used for user feedback, then some text data is available, but the text data is insufficient for effective sentiment analysis
Solution Approach 1:
The patent segments the feedback collection process into multiple data sources: system logs, NLP-generated synthetic text, and ticket data. By dividing the information gathering into these segments, the system combines complementary data types to achieve both availability and precision in sentiment analysis, with each segment contributing different valuable information.
Solution Approach 2:
The system creates a composite feedback material by combining multiple data types (system logs, synthetic NLP text, ticket data) into a unified sentiment analysis input. This composite approach leverages the strengths of each data source while compensating for their individual weaknesses, achieving both sufficient data quantity and high analysis precision.
4Productivity
If automated systems perform sentiment analysis without sufficient text data, then analysis can continue, but the accuracy of sentiment prediction decreases
Solution Approach 1:
The system performs preliminary action by pre-processing system logs and generating synthetic text representations before final sentiment analysis. This preliminary NLP processing creates enriched text data from raw logs, ensuring that when sentiment analysis is performed, sufficient and high-quality text data is already prepared, maintaining both continuity and accuracy.
Solution Approach 2:
An intermediary NLP processing layer transforms sparse system log data into rich synthetic text representations. This intermediary enhances the text data availability without requiring actual user input, enabling continuous accurate sentiment analysis by converting operational logs into meaningful textual sentiment indicators.
Data Source
AI summary
A method implements customer sentiment analysis using logs and feedback. Log data is received. The log data is processed with a text generation model to generate synthesized text. The synthesized text is processed with a sentiment prediction model to generate a sentiment prediction. The sentiment prediction model is trained with a training label received responsive to a similarity score of a training vector meeting a similarity threshold. The sentiment prediction is presented.