Semantic Analysis of Unstructured Customer Feedback
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Solution Overview
Problem
Current methods for analyzing customer feedback, especially in unstructured data from online sources, are inefficient due to the complexity of interpreting textual feedback and the multiple meanings of words in English, making it difficult for organizations to accurately understand customer sentiments and improve their products and services.
Innovation Solution
A system and method for semantic analysis of unstructured data that includes a data extraction module to refine and extract relevant text, an information extraction module using UIMA annotation and open NLP taggers to annotate and tag sentences, and an information visualization module to display ratings in graphical formats, enabling accurate sentiment analysis and rating calculation of customer feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual reading and analysis of customer feedback forms is used, then accurate understanding of feedback quality is achieved, but the process is time-consuming and labor-intensive
Solution Approach 1:
The patent replaces the manual mechanical process of reading and analyzing feedback with an automated computer-based system that uses natural language processing, machine learning algorithms, and text analytics to extract, classify, and analyze customer feedback automatically, eliminating the need for human readers while maintaining or improving analysis accuracy
Solution Approach 2:
The patent introduces an intermediary automated analysis system that acts as a bridge between raw customer feedback and actionable insights, using natural language processing intermediaries to interpret and structure unstructured feedback data before presenting it to decision-makers
2Productivity
If automated text analysis is implemented, then processing efficiency is improved, but accuracy in understanding contextual meaning and multiple word usages deteriorates
Solution Approach 1:
The patent changes the parameters of the analysis system by employing advanced natural language processing techniques, machine learning models, and contextual analysis algorithms that can dynamically adapt to different contexts and meanings of words, allowing automated processing while maintaining semantic understanding accuracy
Solution Approach 2:
The patent uses a composite approach combining multiple analysis techniques including natural language processing, sentiment analysis, topic modeling, and machine learning algorithms to create a robust automated system that can accurately understand contextual meanings and multiple word usages while maintaining high processing efficiency
3Quantity of substance
If comprehensive customer feedback is collected from multiple sources, then data completeness is improved, but data complexity and difficulty of analysis increase
Solution Approach 1:
The patent segments the complex analysis task into distinct modular components including data collection modules for different sources, preprocessing modules for cleaning and standardizing data, analysis modules for different types of insights, and visualization modules, allowing each component to handle specific aspects of the feedback data independently
Solution Approach 2:
The patent creates a universal automated analysis system that can handle multiple types of feedback data from diverse sources (surveys, social media, reviews, support tickets) using the same core natural language processing and machine learning infrastructure, reducing overall system complexity while maintaining comprehensive data collection
Data Source
AI summary
A method and system for semantically analyzing unstructured data in a customer feedback is provided. The method includes extracting data related to customer feedback from one or more input sources. The method further includes assigning weights to the one or more input sources and extracting relevant text from the feedback data. Further, sentences are detected from the customer feedback and are annotated. Thereafter, relevant adjectives are determined and are associated with sentence types. A rating is calculated for each sentence of the relevant text and output is provided in a pre-determined format.