Review Value Evaluation Using Semantic Vector Analysis
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Solution Overview
Problem
Existing methods for evaluating the value of user-generated reviews are inefficient and inaccurate, relying heavily on linguistic analysis tools that struggle with language barriers and lack versatility, leading to difficulties in distinguishing valuable reviews from irrelevant ones, especially for less popular products or services.
Innovation Solution
A method that involves obtaining a vectorized representation of text items in a review, extracting semantic features of consecutive text items, determining their importance, and using these features to automatically evaluate the review's usefulness, thereby providing an efficient and accurate assessment of a review's value.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If traditional linguistic analysis tools are used to evaluate reviews, then the evaluation process can be automated, but the accuracy and effectiveness deteriorate due to language barriers and lack of versatility
Solution Approach 1:
The patent replaces traditional linguistic analysis tools with a deep learning-based neural network model. This substitution enables the system to automatically learn and capture complex semantic patterns from review texts, overcoming the limitations of rule-based linguistic analysis in handling diverse languages and contexts. The neural network processes text through multiple layers (embedding, convolution, pooling, fully connected layers) to produce accurate evaluations across different languages without requiring manual linguistic programming.
2Measurement precision
If manual labeling methods are used to distinguish valuable reviews, then the evaluation accuracy can be maintained, but the time consumption and labor requirements increase significantly
Solution Approach 1:
The patent implements self-service through automatic evaluation where the neural network model independently assesses review value without human intervention. The model processes reviews through its learned representations and classification layers, producing evaluation results autonomously. This eliminates the need for manual labeling while maintaining high accuracy, as the model continuously learns from data and refines its evaluation capabilities without requiring human time or labor.
3Adaptability or versatility
If conventional evaluation methods are used, then the system structure remains simple, but the ability to handle multi-language reviews deteriorates
Solution Approach 1:
The patent achieves universality through a neural network model designed to process multiple languages simultaneously. The model uses universal embedding layers that can represent words from different languages in a shared semantic space, allowing a single system to evaluate reviews in English, Chinese, and other languages without requiring separate specialized models for each language. This multi-functional approach handles linguistic diversity while maintaining a unified system architecture.
4Ease of manufacture
If linguistic analysis tools are relied upon, then the evaluation process can be implemented, but the versatility and effectiveness for less popular products deteriorate
Solution Approach 1:
The patent applies parameter changes by training the neural network model to adapt to different product domains and review patterns. The model learns from diverse data representing various product categories, including niche products, and adjusts its internal parameters to capture domain-specific semantics. This enables the system to maintain high effectiveness across different product types without requiring manual reconfiguration or specialized tools for each domain, as the model's parameters are optimized to handle variability in product-related language.
Data Source
AI summary
According to an exemplary embodiment of the present disclosure, a method, apparatus for evaluating a review, a device and a computer readable storage medium are provided. The method for evaluating a review includes obtaining a first vectorized representation of a set of text items in a review for a target object. The method further includes extracting a semantic feature of at least two consecutive text items of the set of text items based on the first vectorized representation. The method further includes determining a degree of importance of the at least two consecutive text items in a context of the review, and determining a degree of the review helping a user to evaluate the target object, based on the degree of importance and the semantic feature. In this way, an automatic, efficient, and accurate evaluation of the value of a review may be achieved.