Information Processor Customer Need Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing text mining techniques fail to accurately analyze customer needs by solely focusing on word frequency and sentiment, leading to potential misinterpretation of customer opinions and unattractive product features.
Innovation Solution
An information processor with morphological and syntactic analysis units categorizes customer opinions by predetermined needs, using an evaluative word definition unit to set keywords and calculate importance scores based on grammatical parts of speech, providing a more precise analysis of customer needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If word frequency counting is used to analyze customer opinions, then processing simplicity is improved, but analysis accuracy deteriorates
Solution Approach 1:
The patent segments the text analysis process into multiple stages: morphological analysis (word segmentation and part-of-speech tagging), syntactic analysis (phrase structure identification), and semantic analysis (meaning extraction). This segmentation allows each stage to focus on specific aspects, improving overall accuracy while maintaining manageable processing complexity through modular design
Solution Approach 2:
The patent transitions from single-dimensional word frequency counting to multi-dimensional analysis by incorporating syntactic relationships, semantic meanings, and contextual information. This dimensional expansion enables the system to distinguish between high-frequency but irrelevant words and low-frequency but critical evaluative terms, thereby improving analysis accuracy
2Measurement precision
If sentiment analysis of individual words is performed, then evaluation detail is improved, but generalization capability deteriorates
Solution Approach 1:
The patent merges individual word sentiment analysis with phrase-level and document-level analysis. By combining the evaluative meaning of individual words with their syntactic relationships and contextual usage patterns, the system achieves both detailed evaluation of specific terms and generalization across entire customer opinions and product categories
Solution Approach 2:
The system incorporates feedback mechanisms where the analysis results are used to refine the evaluative word definitions and weightings. This iterative process allows the system to learn from specific evaluations and apply the insights more broadly, improving both evaluation detail and generalization capability simultaneously
3Productivity
If high-frequency words are used as evaluation keywords, then processing efficiency is improved, but evaluation accuracy deteriorates
Solution Approach 1:
The patent changes the parameter for keyword selection from pure frequency-based metrics to a composite metric that incorporates frequency, syntactic importance, semantic relevance, and evaluative context. This parameter transformation allows the system to identify keywords that are not necessarily the most frequent but are most indicative of customer satisfaction or dissatisfaction, improving evaluation accuracy while maintaining processing efficiency through automated keyword ranking
Data Source
AI summary
The present invention enables analysis of customer needs with a high level of precision, not by indicating whether the customer opinions are positive or negative, but by quantitatively indicating their levels of importance. An information processor storing customer opinion information containing document data expressing opinions of customers in natural language, includes: a morphological analysis unit which parses document data into individual words, correlates each individual word to a grammatical part of speech, and outputs resultant data; a syntactic analysis unit which uses the data outputted from the morphological analysis unit to analyze content of the document; a clustering unit which uses the processing results from the syntactic analysis unit to categorize and output the customer opinion information according to predetermined customer needs; an evaluative word definition unit which receives, from a user, a setting of a keyword for evaluating the customer needs and an evaluation value for the keyword; and a tally processing unit which calculates a score indicating level of importance of the customer need, by using the customer opinion information categorized by the customer needs, along with the keyword and evaluation value set for the keyword.