Sentiment-Based User Segmentation for Social Network Advertising
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Solution Overview
Problem
Social media advertising lacks effective targeting mechanisms to identify and group users based on their sentiment and behavior towards products, leading to inefficient promotion and detraction of products within online social networks.
Innovation Solution
A method and system that determine user sentiment and categorize users as recommenders, passives, or detractors based on their likelihood to promote or detract from a product, and group them accordingly to present targeted advertisements, utilizing a Net Promoter Score or equivalent scoring systems to assess user behavior and relationships within the social network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional online advertising is used in social networks, then advertising coverage is achieved, but advertising effectiveness and targeting precision are insufficient
Solution Approach 1:
The patent segments users into three distinct categories (promoters, passives, detractors) based on their sentiment analysis towards products. This segmentation enables targeted advertising strategies for each group, improving both targeting precision and advertising effectiveness by treating different user segments differently rather than using blanket advertising approaches.
Solution Approach 2:
The patent changes the parameter of user classification from traditional demographic or behavioral segments to sentiment-based categories (promoters, passives, detractors). This parameter change enables more precise targeting by focusing on users' emotional and attitudinal states towards products, which directly impacts advertising effectiveness.
2Measurement precision
If user sentiment analysis is implemented, then advertising targeting is improved, but system complexity increases
Solution Approach 1:
The patent implements feedback mechanisms where user responses to advertisements and products are continuously analyzed through sentiment analysis. This feedback loop refines user categorization over time, improving categorization accuracy while the automated nature of the feedback processing helps manage system complexity through algorithmic rather than manual processes.
Solution Approach 2:
The patent replaces manual user analysis and categorization with automated sentiment analysis using natural language processing and text analysis. This substitution of mechanical/manual processes with computational algorithms improves categorization accuracy while actually reducing operational complexity, though it increases technical system requirements.
3Measurement precision
If groups are formed based on user relationships and categories, then advertising relevance is improved, but data processing requirements increase
Solution Approach 1:
The patent segments users into three distinct categories (promoters, passives, detractors) based on their sentiment analysis towards products. This segmentation enables targeted advertising strategies for each group, improving both targeting precision and advertising effectiveness by treating different user segments differently rather than using blanket advertising approaches.
Solution Approach 2:
The patent merges multiple data dimensions (user sentiment categories, social relationship data, and advertisement performance metrics) to create comprehensive user groups. This merging of data sources improves advertising relevance by considering both individual user attitudes and their social connections, while the integrated approach manages data processing through unified analytical frameworks.
Data Source
AI summary
An online social network is provided. A sentiment is determined for each of a plurality of users of an online social network (OSN) in relation to a first product. A category is determined for each of the plurality of users based, at least in part, on the sentiment of each of the plurality of users, respectively. A group including a first user and a second user of the plurality of users is generated based, at least in part, on the category of each of the first user and the second user and a relationship within the OSN between the first user and the second user. An advertisement is presented to the first user. An indication is presented to the first user that the advertisement is also presented to the second user.