Dynamic Emotion Identifier Table for Real-Time Sentiment Analysis
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Solution Overview
Problem
Conventional social media analytics engines are slow to identify emerging trends due to limitations in natural language processing tools, particularly with new words, phrases, and symbols, resulting in only about 70% accuracy in sentiment analysis.
Innovation Solution
A computer-implemented method using an emotion identifier table to analyze network messages, separating tokens by spaces or punctuation, determining sentiment scores, and dynamically updating the table with new information to improve accuracy in identifying emerging emotions and attitudes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If natural language processing tools are used to monitor social media trends, then the system can process text data, but the identification speed is slow and accuracy is only about 70%
Solution Approach 1:
The patent segments the message processing into distinct stages: emotion identifier detection, token separation, and sentiment scoring. By dividing the natural language processing pipeline into modular components, the system can efficiently process different elements (emojis, acronyms, regular words) using specialized methods for each, thereby improving both speed and accuracy without requiring complete reprocessing of the entire message.
Solution Approach 2:
The patent introduces an intermediary emotion identifier table that maps emojis and acronyms to sentiment scores. This intermediary structure acts as a lookup mechanism that bridges the gap between raw social media content and sentiment analysis, enabling rapid identification of emotional content without requiring complex natural language processing for every element.
2Adaptability or versatility
If conventional sentiment analysis methods are applied, then the system can analyze existing content, but it fails to accurately identify newly coined words and expressions
Solution Approach 1:
The emotion identifier table is dynamically updated with newly discovered emotion identifiers and their sentiment scores. The system automatically learns from new social media content, adding emerging emojis, acronyms, and expressions to the table as they appear, enabling continuous adaptation to new linguistic trends without manual intervention or retraining of complex models.
Solution Approach 2:
The emotion identifier table is designed to be dynamic rather than static. It continuously evolves by incorporating new emotion identifiers discovered in social media messages, allowing the system to adapt to changing language trends, new emojis, and emerging expressions in real-time, thereby maintaining high accuracy for both established and new content.
3Measurement precision
If the emotion identifier table is continuously updated with new tokens, then the system improves accuracy for emerging trends, but the computational complexity increases
Solution Approach 1:
The patent uses a simple lookup table structure for emotion identifiers that requires minimal computational resources. Rather than employing complex machine learning models, the system uses straightforward dictionary lookups and basic string matching operations. This approach maintains low computational complexity while effectively identifying emerging trends, as the emotion identifier table can be updated with simple key-value pairs representing new emojis, acronyms, and expressions.
Data Source
AI summary
A method including identifying an emotion identifier from a message using an emotion identifier table is provided. The method includes separating emotion identifier from the message, splitting a portion into multiple tokens delimited by a space or a punctuation mark, and determining a score for the message based on an emotion score in the emotion identifier table, and on at least one of the multiple tokens. The method includes adding a token from the multiple tokens to the emotion identifier table based on a contribution from the token to the message score, associating a sentiment score for the token in the emotion identifier table based on the contribution of the token to the message score, and modifying an emotion score of the token when the token is already included in the emotion identifier table based on the contribution of the token to the message sentiment score.