Open Profile Content Identification Using NLP Sentiment Analysis
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Solution Overview
Problem
Existing content identification schemes in online social networks fail to accurately identify relevant content items, especially when user profiles contain short, ambiguous messages, misspelled words, or non-textual content, leading to ineffective advertiser targeting and irrelevant advertisements.
Innovation Solution
A system and method that utilize a natural language processor, sentiment detection processor, and category processor to extract phrases, assign weights, identify user interests and non-interests, and associate labels with user profiles, facilitating the identification of relevant content items based on open profile data and free-form text.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine-based relevance analysis is used to identify content items, then content selection can be automated, but the system fails to accurately identify relevant content when user profiles contain short, ambiguous messages, misspelled words, or non-textual content
Solution Approach 1:
The patent introduces an intermediary system comprising a natural language processor, sentiment detection processor, and category processor that acts as a mediator between the user profile data and content identification. This intermediary system processes free-form text data through multiple specialized components, extracting phrases, detecting sentiment, and categorizing information before content matching occurs, thereby resolving the contradiction between automation and accuracy.
Solution Approach 2:
The content identification system is segmented into distinct functional processors: a natural language processor for extracting and weighting phrases, a sentiment detection processor for identifying user interests and non-interests, and a category processor for associating labels with profiles. This segmentation allows each component to specialize in handling specific aspects of ambiguous or non-textual content, improving overall identification accuracy while maintaining automation.
2Quantity of substance
If broad user base is targeted with general content, then advertiser reach is maximized, but content relevance to individual users decreases
Solution Approach 1:
The system applies local quality by associating specific category labels with individual user profiles based on their unique free-form text data. Each user profile receives customized categorization reflecting their specific interests, preferences, and sentiments extracted from their profile content. This allows advertisers to target content locally to each user's specific interests while maintaining broad overall reach across the platform.
Solution Approach 2:
The system changes the parameter of content targeting from broad demographic categories to fine-grained category labels derived from natural language processing of user profiles. By transforming user profile data into weighted phrases, sentiment indicators, and category associations, the system enables precise content parameter matching that maintains both broad reach and high relevance.
3Measurement precision
If detailed natural language processing is performed on user profiles, then content relevance accuracy improves, but processing complexity and computational resources increase
Solution Approach 1:
The complex natural language processing task is segmented into three specialized processors: phrase extraction with weighting, sentiment detection, and category association. Each processor handles a specific aspect of the analysis, reducing the complexity burden on any single component while collectively achieving high accuracy in user interest identification.
Solution Approach 2:
The system employs self-service mechanisms where the natural language processor automatically extracts and weights phrases from user profiles, the sentiment detection processor autonomously identifies user interests and non-interests, and the category processor independently associates relevant labels. This self-service approach reduces the need for manual intervention and simplifies system operation despite the sophisticated processing involved.
Data Source
AI summary
Open profile data in a user profile, e.g., free-form fields in a user profile, are processed to identify interests and preferences of the user. The interests and preferences are utilized to identify categories associated with the user profile, and content items, e.g., advertisements, can be identified based on the categories.