Message Analyzer Sentiment Verification for Recipient Accuracy
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Solution Overview
Problem
Electronic messaging often results in messages being sent to unintended recipients due to its quick nature, leading to potential embarrassment or violations of privacy and company policies, particularly in professional contexts where sensitive information may be mishandled.
Innovation Solution
A system utilizing sentiment analysis and a knowledge graph to determine whether a message is intended for the recipient, prompting the sender for confirmation if the sentiment is negative or if the topic deviates from previous discussions, ensuring appropriate message delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If electronic messaging is used for fast communication, then communication speed is improved, but message accuracy to intended recipient deteriorates
Solution Approach 1:
The system performs preliminary sentiment analysis and recipient verification before the message is sent. By analyzing the message content and comparing it against the recipient's known preferences and communication patterns in advance, the system can prevent misdirected messages before they occur, resolving the contradiction between fast communication and message accuracy.
Solution Approach 2:
The system provides feedback to the user when a message appears to be misdirected based on sentiment analysis. This feedback loop allows users to correct potential errors before sending, improving message accuracy while maintaining the overall speed of electronic communication through automated analysis.
2Reliability
If sentiment analysis and knowledge graph verification are performed, then message accuracy is improved, but system complexity increases
Solution Approach 1:
The knowledge graph serves multiple functions: it stores recipient information, enables sentiment analysis, verifies message appropriateness, and provides contextual understanding of recipient preferences. By making the knowledge graph multi-functional, the system achieves high message accuracy without adding separate specialized components for each function, thus reducing overall system complexity.
Solution Approach 2:
The system uses the existing knowledge graph infrastructure to perform sentiment analysis and verification autonomously. The knowledge graph itself provides the data and logic needed for verification, eliminating the need for separate complex verification systems and reducing overall system complexity while maintaining high message accuracy.
3Reliability
If message verification is performed, then error reduction is improved, but communication efficiency deteriorates
Solution Approach 1:
The system performs partial verification by focusing sentiment analysis only on key message attributes and comparing them against critical recipient preferences in the knowledge graph. By performing only the essential verification steps rather than exhaustive analysis, the system reduces errors while minimizing impact on communication efficiency.
Data Source
AI summary
Techniques for analyzing a message are described. For example, in some examples a message analyzer is to receive a message including message content and an indication of at least one message recipient, determine a sentiment of the message to generate at least one first sentiment value, determine at least on topic of the message content, and determine that the at least one first sentiment value is less than a second sentiment value associated with the message recipient and the at least one topic of the message content.