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A minimum viable product (MVP) with a new back-end architecture and logic
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A refactored AI algorithm that deciphers emotions in content and users’ data, tags both the content and users with specific character traits based on a scientific classification system of more than 20 character traits and common language taxonomy, and matches content to audiences for targeting
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A Python program based on NLTK for working with human language data
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REST APIs hosted on AWS for scalability and availability
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A light-weight API using Flask
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A powerful visualization of analyzed content and data on an open-source radar chart, customized for the product
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The user’s ability to add text corpora and train the models through their analysis, as well as add pre-trained models and export character visualizations
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Display of audience sizes with specific character traits, links to other research on the text and recommendations on engaging content for the audience
