Weighting Sentiment Information via Interest Attributes
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Solution Overview
Problem
Current methods for analyzing user sentiment about companies' products or services on social media platforms do not differentiate between users based on their relevance, leading to inaccurate market feedback as all users' sentiments are treated equally, regardless of their interest or engagement with the company.
Innovation Solution
A system and method that capture sentiment information from electronic sources, categorize posts based on sentiment, and assign weights to posts based on interest attributes such as social network identification, network size, company name, job title, and social network weight, allowing more relevant posts to be prioritized and providing accurate market feedback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all users' sentiments are treated equally in social media analysis, then the analysis process is simple and straightforward, but the accuracy of market feedback is reduced because irrelevant user sentiments are included
Solution Approach 1:
The patent applies local quality by differentiating the treatment of different users based on their interest attributes. Instead of uniform treatment, users are segmented into different groups (e.g., employees, customers, partners) and each group is weighted differently based on their relevance to the company. This allows the system to maintain simplicity while improving accuracy by applying localized weighting strategies.
Solution Approach 2:
The patent changes the parameter of user weight from a constant value to a variable parameter that depends on interest attributes. By introducing weight factors that vary based on user characteristics (such as job title, department, or engagement level), the system transforms the analysis from a simple count to a weighted evaluation, thereby improving measurement precision without significantly increasing operational complexity.
2Measurement precision
If users are differentiated based on interest attributes, then the accuracy of market feedback is improved, but the complexity of the analysis system increases
Solution Approach 1:
The patent segments users into distinct categories based on their interest attributes (such as employees, customers, partners, or specific departments). This segmentation allows the system to apply different weighting rules to different segments, improving accuracy while maintaining manageable complexity through structured classification. The segmentation approach transforms a complex differentiation problem into a series of simpler, manageable categories.
Solution Approach 2:
The patent introduces interest attributes as intermediary parameters that mediate between the raw sentiment data and the final analysis results. These attributes (such as user role, department, or engagement metrics) serve as intermediate layers that simplify the differentiation process. By using these intermediaries, the system can achieve accurate user differentiation without directly implementing complex analysis algorithms, thereby reducing overall system complexity.
3Measurement precision
If sentiment information is weighted based on user relevance, then the quality of market feedback is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing user interest attributes and their corresponding weight factors before the actual sentiment analysis. User profiles, including their roles, departments, and historical engagement data, are processed in advance to generate weight factors. This preliminary processing allows the main sentiment analysis to proceed more quickly, as the weighting parameters are already ready, thereby reducing processing time while maintaining high feedback quality.
Solution Approach 2:
The patent implements dynamics by making the weight factors adjustable and adaptable rather than fixed. The system can dynamically adjust weight factors based on changing business priorities, user behaviors, or market conditions. This flexibility allows the system to optimize processing efficiency by adjusting the complexity of weighting in real-time, balancing between analysis quality and processing time based on current operational requirements.
Data Source
AI summary
Weighting sentiment information includes capturing sentiment information of a post from an electronic source, categorizing the post into categories based on the sentiment information, and assigning a weight to the post based on an interest attribute.