Method for identifying and quantifying sentiment bias, and apparatus for constructing the sentiment probability graph via agreeable cuts in sociotechnical networks

Description:

This technology introduces a novel approach for analyzing social networks by evaluating sentiments between nodes (users or content) to detect bias and predict promotional opportunities. It constructs probability graphs from sentiments expressed as weights on edges between nodes, facilitating the identification of sentiment bias and influence dynamics within social networks.

Key Advantages:

• Enables detailed sentiment analysis within social networks.
• Identifies and quantifies sentiment biases effectively.
• Predicts promotion opportunities based on network dynamics.
• Utilizes a unique method of constructing balanced graphs for analysis.
• Facilitates understanding of influence among groups or individuals.

Problems Solved:

• Detection of inequity and bias in social interactions.
• Identification of key influencers and their impact on group opinions.
• Quantification of sentiment bias and its effects on social network dynamics.
• Prediction of promotional success within social networks.

Market Applications:

• Social media monitoring and analysis tools.
• Marketing and promotional strategy development.
• Human resources and organizational behavior analysis.
• Public opinion research and political campaign strategy.
• Content recommendation algorithms.