Realtime Product Reputation Measurement Using Multi-Task NLP
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Solution Overview
Problem
Current methods for measuring product/service reputation from open web data face challenges such as large volumes of unstructured data, noise, and potential bias, making it difficult to extract meaningful and accurate insights.
Innovation Solution
A system utilizing natural language processing (NLP) and multi-task machine learning models to cyclically refine searches, recognize product/service aspects, and classify sentiment, filtering out noise and ensuring data relevance and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If sentiment analysis and social listening tools are used to measure product reputation, then insights into customer sentiment can be obtained, but the volume of unstructured data and noise increases
Solution Approach 1:
The patent extracts only the relevant information (sentiment and aspect data) from the vast amount of unstructured social media data using NLP models, separating useful insights from noise and irrelevant content
Solution Approach 2:
The patent introduces NLP models and machine learning algorithms as intermediary processing layers between raw social media data and business insights, automatically filtering and transforming unstructured data into structured actionable information
2Measurement precision
If more data is collected from open web sources, then measurement accuracy improves, but data processing complexity and time requirements increase
Solution Approach 1:
The system performs self-service processing by automatically collecting, filtering, and analyzing data from multiple sources using integrated NLP models, eliminating the need for manual data processing and reducing operational complexity
Solution Approach 2:
The patent employs a multi-functional platform that simultaneously collects data from diverse sources (social media, reviews, forums), processes different data types, and generates various insights (sentiment analysis, aspect extraction, reputation scoring) through a single integrated system
3Reliability
If manual data collection and analysis methods are used, then data quality can be controlled, but productivity and real-time measurement capability decrease
Solution Approach 1:
The patent replaces manual data collection and analysis processes with automated NLP models, web scrapers, and machine learning algorithms that continuously monitor and process data in real-time without human intervention
Solution Approach 2:
The system maintains continuous operation by automatically and continuously collecting, processing, and analyzing data from multiple sources in real-time, providing ongoing reputation measurements without interruption
4Loss of information
If search filters are applied to narrow down data, then relevant information is improved, but potential bias and loss of context may occur
Solution Approach 1:
The patent dynamically adjusts search parameters and filter criteria based on the analysis context, product category, and data characteristics to optimize the balance between relevance and accuracy without introducing bias
Solution Approach 2:
The system incorporates feedback loops that continuously monitor data quality, analysis results, and model performance, automatically refining search filters and processing parameters to maintain accuracy while reducing bias
Data Source
AI summary
A system and method measures in real time the reputation of products or services based on customer reviews and social media mentions. The method includes cyclically refining a search to collect, using a natural language processing (NLP) model, data relating to the products or services, and simultaneously recognizing product/service aspects and classifying sentiment for the collected data, using a single multi-task machine learning model.