Electronic Apparatus Sentiment Analysis Using Multi-Algorithm NLP
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Solution Overview
Problem
Existing methods for analyzing user feedback in online shopping using machine learning algorithms, such as deep learning, often fail to accurately match the emotional tone of user reviews with the actual sentiment towards products, leading to inaccurate identification of positive or negative feedback.
Innovation Solution
An electronic apparatus that performs natural language processing (NLP) using at least two different algorithms to identify positive or negative feedback by analyzing keyword information, feedback titles, and rating information, with a weighting system to enhance accuracy, and displays the results, allowing for user input to filter feedback by product specifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single NLP algorithm is used to analyze user feedback, then the device complexity is low, but the measurement precision of sentiment identification is insufficient
Solution Approach 1:
The patent combines multiple NLP algorithms (including deep learning models and traditional text analysis methods) into a unified sentiment analysis system. This merging of different algorithmic approaches enables the system to leverage the strengths of each method, thereby improving overall sentiment identification accuracy while managing complexity through integrated architecture design.
Solution Approach 2:
The patent implements a multi-functional NLP processing system that can handle various types of user feedback (reviews, ratings, comments) using a comprehensive algorithmic framework. The system is designed to perform multiple analysis functions simultaneously, making it universally applicable to different feedback formats and languages while maintaining high measurement precision.
2Measurement precision
If multiple NLP algorithms are applied to user feedback, then the measurement precision of sentiment identification improves, but the loss of time for processing increases
Solution Approach 1:
The patent implements preliminary text preprocessing and feature extraction before applying multiple NLP algorithms. By preparing the data in advance (tokenization, stopword removal, lemmatization, and feature vector creation), the system reduces the computational burden during the actual sentiment analysis phase, thereby minimizing processing time loss while maintaining high identification accuracy.
Solution Approach 2:
The patent applies a tiered approach where not all NLP algorithms are executed for every feedback item. The system uses lightweight filtering and preliminary analysis to identify cases requiring full multi-algorithm processing, applying excessive action only where necessary to achieve high precision without uniformly incurring the time cost across all inputs.
3Reliability
If comprehensive user feedback analysis is performed, then the reliability of product evaluation improves, but the device complexity increases
Solution Approach 1:
The patent segments the comprehensive feedback analysis system into distinct functional modules: data collection module, preprocessing module, multiple algorithm processing module, result aggregation module, and visualization module. This segmentation allows each component to specialize in specific tasks, improving overall evaluation reliability while managing complexity through modular architecture that enables independent development and maintenance of each segment.
Solution Approach 2:
The patent introduces intermediary components such as standardized data interfaces, result aggregation layers, and normalization mechanisms that mediate between the complex multi-algorithm processing and the final evaluation output. These intermediaries simplify the interface between different system components and provide a unified view of results, thereby improving reliability without exposing the full complexity of the underlying system architecture.
Data Source
AI summary
An electronic apparatus is disclosed. The electronic apparatus includes a display, a storage in which keyword information by product specification is stored, and a processor configured to obtain user feedback on the product by crawling a website, identify positive feedback or negative feedback among the user feedback corresponding to the keyword information by specification by performing natural language processing (NLP) to which at least two different algorithms are applied, and display a result of the identification through the display.