An application of Machine Learning is covered in the news lately: movie script analysis.
Solving Equation of a Hit Film Script, With Data
They "compare the story structure and genre of a draft script with those of released movies, looking for clues to box-office success". However, the comments reveal that the general population (at least of the commenters) dislikes the concept for fear of anti-creativity.
Comments like these sum up the overall sentiment:
Ouch.
You be the judge whether this is a good application or not.
I tend to bias towards answers like this from the comments (sadly this was only 1 of 2 positive comments at the time of my reading; the other one was from the CEO of the script analysis business):
I think it also never helps the image of such machine learning practitioners when the journalist tries to paint him with an antagonist brush, such as "chain-smoking" and "taking a chug of Diet Dr Pepper followed by a gulp of Diet Coke and a drag on a Camel". Reminded me somewhat of another writer's writing style when covering analytics.
Solving Equation of a Hit Film Script, With Data
They "compare the story structure and genre of a draft script with those of released movies, looking for clues to box-office success". However, the comments reveal that the general population (at least of the commenters) dislikes the concept for fear of anti-creativity.
Comments like these sum up the overall sentiment:
"Using old data to presage a current idea is both terrible and foolish. It is to writing what Denny's is to fine dining - mediocrity run wild."
"Data crunchers will take the art out of everything. Paint-by-numbers."
Ouch.
You be the judge whether this is a good application or not.
I tend to bias towards answers like this from the comments (sadly this was only 1 of 2 positive comments at the time of my reading; the other one was from the CEO of the script analysis business):
"I'm sure people have all sots of assumptions about what audiences like already. This data could be a tool to look deeper into these assumptions. Film makers have always wondered about consumer taste. It is a business. When commerce and art mix, there are inevitable compromises. This tool helps people see possible preferences based on past behavior. Information should never frighten us. It is how this information is applied that most deserves our attention."
I think it also never helps the image of such machine learning practitioners when the journalist tries to paint him with an antagonist brush, such as "chain-smoking" and "taking a chug of Diet Dr Pepper followed by a gulp of Diet Coke and a drag on a Camel". Reminded me somewhat of another writer's writing style when covering analytics.
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