Sentiment Topic Modeling for Customer Review Analysis
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Solution Overview
Problem
In large-scale retail and e-commerce operations, the voluminous nature of customer reviews makes human assessment burdensome, necessitating a tool to identify important issues within the data for product improvement efforts.
Innovation Solution
A sentiment topic modeling tool that categorizes reviews by product and concern areas, generates topic networks, determines themes, and abstracts data into focus areas for business improvements, using techniques like text cleaning, sentiment classification, and bi-gram analysis to extract key issues from large volumes of customer feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human study and assessment of each individual review is performed, then measurement precision is improved, but loss of time increases and productivity decreases
Solution Approach 1:
The patent replaces the mechanical human assessment system with an automated text mining and sentiment analysis system. The system uses natural language processing algorithms to automatically extract topics, sentiments, and insights from customer reviews, eliminating the need for manual human analysis while maintaining measurement precision through structured computational methods.
Solution Approach 2:
The patent introduces an intermediary automated analysis platform that sits between the raw review data and the business insights. This intermediary system processes voluminous reviews through multiple stages including text cleaning, topic modeling, sentiment classification, and insight generation, transforming unstructured data into actionable business intelligence without requiring direct human involvement in each review assessment.
2Productivity
If automated text processing is applied, then productivity is improved and loss of time is reduced, but measurement precision may worsen
Solution Approach 1:
The patent segments the automated analysis process into distinct functional modules: text preprocessing, topic modeling, sentiment analysis, and insight generation. Each module performs a specific function with optimized algorithms, allowing the system to process large volumes of reviews efficiently while maintaining precision through specialized processing at each stage rather than a single monolithic approach.
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously refines its analysis based on processed data patterns. The topic modeling and sentiment analysis algorithms learn from the corpus of reviews and adjust their parameters to improve accuracy, ensuring that automated processing maintains or enhances measurement precision while achieving high productivity.
Data Source
AI summary
A disclosed sentiment topic modeling tool identifies issues within voluminous customer review data, based on particular categories of review submission (e.g., particular products and experiences) and concern areas (e.g., quality, performance, suitability of features), and abstracts the extracted topic data into a manageable set of focus areas for business operations improvements. An exemplary process includes: receiving a plurality of reviews; selecting a category of review to use for a topic network; selecting a number of topics for generating the topic network; generating, based at least on the selected category and the selected number of topics, the topic network; generating a plurality of topic networks in a topic network group, and determining a set of themes for the group. Additional network groups are generated, and a set of themes is determined for each. A set of focus areas is determined, based at least on the sets, and reports are generated.