Sentiment Mining for Ecommerce DSR Rating Reconciliation
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Solution Overview
Problem
Ecommerce platforms face inconsistencies in user-provided feedback due to misunderstandings of numerical DSR ratings, often resulting in anomalous or contradictory ratings that do not align with the sentiment expressed in the feedback text, influenced by cultural and geographical biases.
Innovation Solution
Implementing sentiment mining techniques to extract and reconcile sentiment from feedback text with numerical DSR ratings, using positive and negative lexicons, contrasting conjunctions, and phrase-level emotion analysis to normalize DSRs by adjusting weights based on discrepancies between text sentiment and ratings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users provide numerical DSR ratings independently of text sentiment analysis, then the rating system is simple to operate, but the accuracy and reliability of seller performance assessment deteriorates due to misunderstandings and biases
Solution Approach 1:
The patent introduces sentiment analysis as an intermediary component that processes feedback text to extract objective sentiment scores. This mediator bridges the gap between user-provided numerical ratings and actual sentiment expressed in text, reducing the impact of user misunderstandings and cultural biases while maintaining the simplicity of the original rating system.
Solution Approach 2:
The system implements feedback by comparing the numerical DSR rating with the sentiment score derived from text analysis. When discrepancies are detected between the numerical rating and the actual sentiment expressed in text, the system adjusts or reweights the rating to better reflect the true sentiment, thereby improving measurement precision without complicating the user interface.
2Productivity
If the system uses only numerical DSR ratings for seller assessment, then the assessment process is simple and fast, but the reliability deteriorates due to anomalous and inconsistent ratings from user misunderstandings
Solution Approach 1:
Sentiment analysis serves as an intermediary that processes feedback text to generate objective sentiment scores. This intermediary layer filters out anomalous ratings caused by user misunderstandings while preserving the speed of automated assessment, as the sentiment analysis can be performed efficiently using natural language processing techniques.
Solution Approach 2:
The system dynamically changes the weight or influence of numerical DSR ratings based on the consistency between the numerical rating and the sentiment score derived from text analysis. When the numerical rating aligns with the sentiment, it retains full weight; when inconsistent, its weight is reduced or adjusted, thereby improving reliability while maintaining automated processing speed.
3Measurement precision
If the system performs detailed sentiment analysis on feedback text, then the accuracy of DSR ratings improves, but the complexity of the system increases
Solution Approach 1:
The sentiment analysis system is segmented into modular components: text preprocessing, sentiment score extraction, consistency checking, and rating adjustment. This segmentation allows the system to achieve high measurement precision through detailed analysis while managing complexity through modular architecture, where each component performs a specific function and can be independently optimized or replaced.
4Stability of the object's composition
If the system adjusts DSR ratings based on sentiment analysis, then the consistency between ratings and feedback text improves, but the time required to process feedback increases
Solution Approach 1:
The system dynamically adjusts the processing parameters based on the level of inconsistency detected between numerical ratings and sentiment. For highly consistent feedback, minimal processing time is required. For inconsistent feedback, the system applies more sophisticated analysis and adjustment algorithms, thereby optimizing the balance between consistency improvement and processing time.
Data Source
AI summary
Reconciling detailed transaction feedback by detecting a rating of a transaction, where the rating indicates a negative experience, mining the sentiment of words in feedback text that is included with or as part of the rating to detect whether the words indicate positive sentiment or negative sentiment, responsive to determining that the words in the feedback text indicate that the feedback text connotes a positive sentiment, adjusting the rating of the transaction. The mining may include testing words in the feedback text to detect whether the words indicate positive sentiment or negative sentiment by calculating a sentiment score.