Unstructured Text Theme and Sentiment Analysis System
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Solution Overview
Problem
Current analysis tools for unstructured computer text are inefficient in identifying themes and sentiment, requiring substantial manual processing and lacking accurate sentiment analysis, as well as the integration of quantitative data for insightful understanding.
Innovation Solution
A system that analyzes unstructured computer text by extracting phrases, ranking them based on frequency and click data, and grouping them to determine themes and sentiment, using a combination of searched phrases logs and phrase click logs, along with theme and tonality dictionaries for sentiment analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual processing is used to analyze unstructured text, then analysis depth can be achieved, but processing time and labor requirements increase substantially
Solution Approach 1:
The patent replaces manual mechanical text analysis with an automated computer-based system that uses natural language processing algorithms, statistical models, and machine learning to perform theme identification and sentiment analysis, thereby eliminating the need for substantial manual processing while maintaining or improving analysis depth
Solution Approach 2:
The system enables unstructured text to be analyzed automatically through programmed algorithms that self-process the text data, identifying themes, extracting sentiments, and generating insights without requiring human intervention for each analysis task, thus resolving the contradiction between analysis quality and processing time
2Ease of manufacture
If basic thematic tagging is performed, then some text categorization is achieved, but accurate sentiment analysis and quantitative integration remain insufficient
Solution Approach 1:
The patent combines multiple analysis functions into a unified system that simultaneously performs thematic tagging, sentiment analysis, and quantitative data integration. The system merges theme identification, tonality detection, and statistical analysis into a cohesive process that produces comprehensive insights rather than isolated text categories
Solution Approach 2:
The system transforms text analysis from simple categorical tagging to multi-dimensional measurement by introducing sentiment scores, tonality metrics, and quantitative parameters. This parameter expansion enables accurate sentiment analysis by measuring emotional intensity, polarity, and other nuanced aspects beyond basic theme classification
3Loss of information
If comprehensive text analysis is performed to identify themes and sentiment, then insightful understanding is achieved, but system complexity increases
Solution Approach 1:
The patent divides the complex text analysis process into distinct modular components: theme identification module, sentiment analysis module, tonality detection module, and statistical integration module. Each module performs a specific function and can be independently developed, tested, and optimized, thereby managing system complexity while achieving comprehensive analysis
Solution Approach 2:
The system introduces intermediate processing layers including tokenization, phrase extraction, and candidate phrase generation that mediate between raw unstructured text and final analytical insights. These intermediary steps break down the complex analysis task into manageable stages, reducing overall system complexity while preserving insightful understanding
Data Source
AI summary
Methods and apparatuses are described for analyzing unstructured computer text for theme generation to determine sentiment. A computer store stores unstructured text that is delimited, a searched phrases log, and a phrase click log. A computer server extracts phrases from the unstructured delimited text by splitting each line of the unstructured delimited text into one or more phrases. The computer server generates tokens from the unstructured delimited text, where the tokens comprise segments of the unstructured delimited text. The computer server determines one or more themes present in the unstructured delimited text.