Emoji Disambiguation via Social Clustered Topic Models
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Solution Overview
Problem
Emojis often have multiple meanings depending on context, vernacular, and user interactions, leading to ambiguity in online communications, as existing tools fail to provide adequate solutions for disambiguation.
Innovation Solution
A method and system that construct a social clustered topic model (SCTM) to analyze interactions and determine the intended meaning of emojis by computing probability scores based on user relationships and content analysis, incorporating contextual references to refine predictions and provide users with probable meanings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If emojis are used in online communications to convey emotions and meanings, then communication expressiveness is improved, but ambiguity in emoji interpretation increases
Solution Approach 1:
The system provides feedback by analyzing the contextual meaning of emojis through social clustered topic models and presenting the most probable interpretations to users, allowing them to understand the intended meaning based on patterns from similar online interactions
Solution Approach 2:
The patent introduces an intermediary system that acts as a mediator between emoji senders and receivers. This system uses social clustered topic models to analyze contextual patterns and determine the most probable meaning of ambiguous emojis, presenting multiple interpretations ranked by probability to resolve the ambiguity
2Measurement precision
If context analysis is performed to disambiguate emoji meanings, then interpretation accuracy is improved, but computational complexity increases
Solution Approach 1:
The system segments the complex task of emoji disambiguation into manageable components: constructing social clustered topic models from online interactions, analyzing specific messages against these models, and generating probability-based interpretations. This segmentation allows the system to handle complexity through modular processing
Solution Approach 2:
The system performs preliminary action by pre-constructing social clustered topic models from large volumes of online interaction data before actual emoji disambiguation is needed. This pre-processing creates a knowledge base that speeds up real-time emoji interpretation without requiring complex computations during the actual disambiguation process
Data Source
AI summary
A new data structure of a social clustered topic model comprising new data is constructed, the new data being generated from content of online interactions using a processor and a memory. A social media message is analyzed, using a processor and a memory, to compute an emoji probability score reflecting a degree of correspondence between an emoji present in the social media message and a meaning extracted from the new social clustered topic model. The social media message is modified automatically, when the emoji probability score is above a threshold probability score, by adding additional data to the social media message, the additional data including the meaning.