Sentiment Analysis for Electronic Messages
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Solution Overview
Problem
Electronic messages lack contextual cues, leading to potential misinterpretation of tone and urgency, as recipients cannot discern the sender's mood or intent due to the absence of body language and tone in written communication.
Innovation Solution
A method and system that analyze electronic messages by parsing sub-constructs using sentiment dictionaries and parsing rules to assign sentiment indicators and scores, providing a final sentiment assessment and suggesting alternative language to convey intended tone effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If electronic messages are used for communication, then communication efficiency is improved, but tone and intent are misinterpreted due to lack of body language and tone
Solution Approach 1:
The patent introduces sentiment analysis technology as an intermediary system that processes electronic messages to extract and analyze sentiment information. The system uses natural language processing to identify sentiment indicators, assign sentiment scores, and generate sentiment assessments that are then presented to users, thereby mediating the loss of tone and intent information in electronic communication
Solution Approach 2:
The patent replaces the mechanical/physical cues of face-to-face communication (body language, tone of voice) with computational analysis methods. Instead of relying on physical presence, the system uses algorithmic sentiment analysis to detect and interpret emotional content in text, substituting physical communication mechanisms with information processing mechanisms
2Loss of information
If sentiment analysis is added to electronic messages, then tone and intent understanding is improved, but system complexity increases
Solution Approach 1:
The patent segments the sentiment analysis process into distinct modular components: message parsing to identify sub-constructs, sentiment indicator detection, sentiment scoring, and final sentiment assessment generation. Each component performs a specific function and can be independently developed and maintained, reducing overall system complexity through functional decomposition
Solution Approach 2:
The system enables users to self-configure sentiment analysis by allowing them to define custom sentiment indicators, scoring rules, and assessment criteria according to their specific communication needs. This self-service capability reduces the need for complex pre-configuration and customization support, simplifying system deployment and adaptation
Data Source
AI summary
Provided are techniques for determining a sentiment of an electronic message. The electronic message is parsed to identify one or more sub-constructs. For at least one of the sub-constructs that is not false-positive, a sentiment indicator is assigned from a set of types of sentiment indicators, and a score is assigned for the sentiment indicator. A final score is obtained for at least one type of sentiment indicator in the electronic message by summing scores for that type of sentiment indicator. Based on the final score for the at least one type of sentiment indicator, a sentiment of the electronic message is identified.