You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
When we have the CSV file with lots of generated stories (see #2) we can start analysing. Is it possible to extract info computationally?
Can we try some kind of sentiment analysis?
Word embeddings?
Simple word clouds showing the most common words for different languages?
Identify person names and place names and visualise that somehow?
I also very much want to analyse the last sentence, which is always (I think!) about the lessons learned. Any way to computationally analyse that would be great - but we may need to handcode it. Some words are clearly very frequent, e.g. "friendship", "teamwork", "bravery". Maybe with enough generated stories even just a visualisation showing frequency would work?
When we have the CSV file with lots of generated stories (see #2) we can start analysing. Is it possible to extract info computationally?
I also very much want to analyse the last sentence, which is always (I think!) about the lessons learned. Any way to computationally analyse that would be great - but we may need to handcode it. Some words are clearly very frequent, e.g. "friendship", "teamwork", "bravery". Maybe with enough generated stories even just a visualisation showing frequency would work?
My first attempts at data visualisation are in https://github.com/MachineVisionUiB/GPT-stories/blob/main/script/kids-stores-lessons.R - just using the first data I got using the chat interface of ChatGPT and handcoding "lessons learned".
The text was updated successfully, but these errors were encountered: