Personality-Based Sentiment Analysis for Email Prioritization
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Solution Overview
Problem
Current email systems struggle with ambiguity in information due to the choice of words and the sender's personality, making it difficult to accurately capture and prioritize emotions in emails.
Innovation Solution
Implementing a Personality-Based Sentiment Analysis (PBSA) system that combines sentiment analysis with personality metrics from the sender's social media activity to provide a more accurate emotional score, represented as emoticons and sounds, allowing for better email prioritization and filtering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sentiment analysis is performed on natural language input, then emotional information can be extracted, but ambiguity remains due to word choice and sender personality
Solution Approach 1:
The patent combines sentiment analysis results with personality metric analysis by merging the output of two separate analyzers (sentiment analyzer and personality analyzer) to produce a personality-based sentiment score. This integration allows the system to compensate for the limitations of sentiment analysis alone by incorporating contextual information about the sender's personality traits, thereby reducing ambiguity and information loss in emotional context detection.
2Measurement precision
If personality metrics are integrated into sentiment analysis, then accuracy improves, but system complexity increases
Solution Approach 1:
The patent divides the personality-based sentiment analysis system into distinct modular components: a sentiment analyzer that processes emotional content, a personality analyzer that evaluates personality metrics from social media data, and a combining mechanism that integrates both analyses. This segmentation allows each component to specialize in specific tasks, improving overall accuracy while maintaining manageable system complexity through clear separation of concerns.
3Loss of information
If social media data is used to determine personality metrics, then sender personality can be captured, but data privacy concerns arise
Solution Approach 1:
The patent introduces an intermediary layer (the personality analyzer) that processes social media data to extract personality metrics without directly exposing raw personal information. This intermediary transforms potentially sensitive social media content into aggregated personality trait scores, thereby capturing necessary personality information while reducing privacy risks associated with handling raw social media data.
Data Source
AI summary
A sentiment analyzer obtains natural language media input and determines sentiment of the natural language media input. A personality analyzer obtains data indicative of a personality of an originator of the natural language media input and determines a personality metric of the originator of the natural language media input. The sentiment of the natural language media input and the personality metric of the originator of the natural language media input are combined to obtain a personality-based sentiment of the natural language media input. The natural language media input is provided to a receiver together with a representation of the personality-based sentiment of the natural language media input.