Is your enhancement related to a problem? Please describe.
As related to (perhaps a corollary of) #489 and as mentioned in #81 and #117, we could look to leverage Sentiment Analysis as an alternative to the approach referenced in 489 to identify the "tone" for a site and as input to a prompt for content generation to ensure future content generations align to that "tone".
Designs
Various sentiment analysis tools are available within AI service providers we already integrate with (e.g., Watson's NLU emotion and sentiment text analytics features, Azure's Language sentiment analysis feature), with other AI providers that we've considered (e.g. Amazon's Comprehend sentiment feature), and likely others that would arise with some searching on the topic.
The concepts that we could explore with a rough proof of concept / prototype to see if this could work as an alternative for #489 is as follows:
utilize Sentiment Analysis on the existing site content to identify the average plotting of said content across various sentiment scales (formal-to-informal, humorous-to-non-humorous, academic-to-non-academic, happy-to-sad, optimistic-to-pessimistic, etc.) and store that as the site "tone"
present those averages of existing content on those different sentiment options with the ability to adjust where the content ideally exists on respective scales to match the "desired tone" for a site
utilize the stored sentiment option scales in prompting for content generation to attempt to affect the resulting output from an AI Service provider to match the "tone" for site content
Describe alternatives you've considered
One approach for a proof of concept here would be to have stored values for where sentiment options are on respective scales and send the same prompt for content generation with wildly different scale values to see if the resulting output content varies and appears to match the "desired tone" for the sentiment option scale values. If that appears to work, then building out the proof of concept further for assessment before building more formally into ClassifAI would be helpful.
Code of Conduct
[X] I agree to follow this project's Code of Conduct
Is your enhancement related to a problem? Please describe.
As related to (perhaps a corollary of) #489 and as mentioned in #81 and #117, we could look to leverage Sentiment Analysis as an alternative to the approach referenced in 489 to identify the "tone" for a site and as input to a prompt for content generation to ensure future content generations align to that "tone".
Designs
Various sentiment analysis tools are available within AI service providers we already integrate with (e.g., Watson's NLU emotion and sentiment text analytics features, Azure's Language sentiment analysis feature), with other AI providers that we've considered (e.g. Amazon's Comprehend sentiment feature), and likely others that would arise with some searching on the topic.
The concepts that we could explore with a rough proof of concept / prototype to see if this could work as an alternative for #489 is as follows:
Describe alternatives you've considered
One approach for a proof of concept here would be to have stored values for where sentiment options are on respective scales and send the same prompt for content generation with wildly different scale values to see if the resulting output content varies and appears to match the "desired tone" for the sentiment option scale values. If that appears to work, then building out the proof of concept further for assessment before building more formally into ClassifAI would be helpful.
Code of Conduct