Conversational System Sentiment Analysis for Product Attributes
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Solution Overview
Problem
Conversational systems face inefficiencies in providing user responses as they often assume mentioned product attributes are desirable, failing to account for user sentiment, which can lead to unsatisfactory interactions and increased dialog turns.
Innovation Solution
A method and system that utilize conjoint analysis to determine the desirability of product attributes by extracting features, analyzing user reviews, calculating desirability scores, and generating responses that consider user sentiment, thereby guiding the conversation towards relevant information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conversational systems assume mentioned product attributes are desirable without analyzing user sentiment, then the system operation is simple and fast, but the response accuracy and user satisfaction deteriorate
Solution Approach 1:
The system performs preliminary sentiment analysis on product attributes before generating responses. By pre-processing and storing sentiment scores for product features in advance, the system avoids complex real-time analysis during user interactions, thus improving response accuracy while maintaining operational efficiency
Solution Approach 2:
The patent introduces an intermediary sentiment analysis layer between the user query and the product information. This intermediary component analyzes user sentiment toward product attributes and uses it to modulate the conversational response, improving accuracy without requiring complete system redesign
2Loss of information
If conversational systems do not consider user sentiment toward product attributes, then the dialog flow is short and efficient, but the information relevance and user satisfaction worsen
Solution Approach 1:
The system pre-analyzes and stores sentiment information about product attributes before user queries arrive. This preliminary preparation allows the system to quickly retrieve and apply relevant sentiment data during conversations, reducing dialog turns while maintaining high information relevance
Solution Approach 2:
The system incorporates sentiment feedback from user reviews into the conversational response generation process. By continuously learning from user sentiment expressions and adjusting responses accordingly, the system improves information relevance while maintaining efficient dialog flow
Data Source
AI summary
A method, computer system, and computer program product for determining a desirability of a product attribute are provided. The embodiment may include extracting a plurality of product features corresponding to one or more products. The embodiment may also include analyzing a plurality of product reviews to determine a sentiment toward the extracted product features. The embodiment may further include calculating a desirability score for each product feature. The embodiment may also include, in response to receiving a user query associated with a product, identifying a product feature to which the received user query relates. The embodiment may further include generating a response to the received user query based on the calculated desirability score.