Automated Image Selection Using Sentiment Analysis
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Solution Overview
Problem
Existing methods for selecting images to accompany text in social media posts are laborious and fail to account for themes or emotions, often resulting in inappropriate image choices, as they do not consider prior user activity or sentiment.
Innovation Solution
A system that receives user text, identifies search terms, selects candidate images from databases based on text analysis, and presents them for selection, incorporating sentiment analysis and user history to improve image choice accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated image selection is implemented, then productivity is improved, but device complexity increases
Solution Approach 1:
The system segments the image selection process into distinct modules: sentiment analysis module that processes text emotion, topic modeling module that identifies subject matter, candidate image retrieval module that searches databases, and ranking module that scores matches. This modular segmentation enables automated high-speed image selection while keeping each component's complexity manageable and independently optimizable.
Solution Approach 2:
The patent introduces intermediary components including a sentiment lexicon that mediates between text input and image selection, topic models that bridge text semantics and image characteristics, and scoring functions that act as intermediaries between candidate images and final selection. These intermediaries translate complex text-image matching into structured computational steps, improving automation while organizing system complexity.
2Measurement precision
If sentiment analysis and user history tracking are incorporated, then image selection accuracy is improved, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by pre-computing and storing user sentiment profiles from historical data, pre-indexing images with sentiment tags and topic keywords, and pre-building sentiment lexicons. When a new text requires image selection, these pre-prepared resources enable rapid accurate matching without re-processing all historical data, thus improving accuracy while minimizing additional processing time.
Solution Approach 2:
The patent dynamically adjusts processing parameters based on text characteristics: for short texts, it uses simplified sentiment scoring; for long texts, it employs comprehensive topic modeling. The system also adjusts the number of candidate images retrieved and the depth of sentiment analysis based on confidence thresholds, optimizing the balance between selection accuracy and processing time for different input scenarios.
Data Source
AI summary
A system for selecting an image to accompany text from a user in connection with a social media post. The system is capable of receiving text from the user, identifying one or more search terms based on the text, identifying candidate images from images in one or more image databases using the search terms, presenting one or more candidate images to the user, receiving from the user a selected image from the one or more candidate images, generating the social media post comprising the selected image and the user-submitted text, and transmitting the social media post for display.