AI tool may predict movies' future ratings

Date:
November 17, 2020
Source:
University of Southern California
Summary:
Researchers, armed with artificial intelligence tools, can rate a
movie's content in a matter of seconds, based on the movie script
and before a single scene is shot.



FULL STORY ========================================================================== Movie ratings can determine a movie's appeal to consumers and the size of
its potential audience. Thus, they have an impact on a film's bottom line.

Typically, humans do the tedious task of manually rating a movie based
on viewing the movie and making decisions on the presence of violence,
drug abuse and sexual content.


==========================================================================
Now, researchers at the USC Viterbi School of Engineering, armed with artificial intelligence tools, can rate a movie's content in a matter of seconds, based on the movie script and before a single scene is shot. Such
an approach could allow movie executives the ability to design a movie
rating in advance and as desired, by making the appropriate edits on a
script and before the shooting of a single scene. Beyond the potential financial impact, such instantaneous feedback would allow storytellers
and decision-makers to reflect on the content they are creating for the
public and the impact such content might have on viewers.

Using artificial intelligence applied to scripts, Shrikanth Narayanan, University Professor and Niki & C. L. Max Nikias Chair in Engineering,
and a team of researchers from the Signal Analysis and Interpretation
Lab (SAIL) at USC Viterbi, have demonstrated that linguistic cues can effectively signal behaviors on violent acts, drug abuse and sexual
content (actions that are often the basis for a film's ratings) about
to be taken by a film's characters.

Method: Using 992 movie scripts that included violent, substance-abuse
and sexual content, as determined by Common Sense Media, a non-profit organization that rates and makes recommendations for families and
schools, the SAIL research team trained artificial intelligence to
recognize corresponding risk behaviors, patterns and language.

The AI tool created receives as input all the script, processes it through
a neural network and scans it for semantics and sentiment expressed. In
the process, it classifies sentences and phrases as positive, negative, aggressive and other descriptors. The AI tool automatically classifies
words and phrases into three categories: violence, drug abuse and
sexual content.



========================================================================== Victor Martinez, a doctoral candidate in computer science at USC Viterbi
and the lead researcher on the study, which will appear in The Proceedings
of the 2020 Conference on Empirical Methods in Natural Language Processing said, "Our model looks at the movie script, rather than the actual scenes, including e.g.

sounds like a gunshot or explosion that occur later in the production
pipeline.

This has the benefit of providing a rating long before production
to help filmmakers decide e.g. the degree of violence and whether it
needs to be toned down." The research team also includes Narayanan,
a professor of electrical and computer engineering, computer science
and linguistics, Krishna Somandepalli, a Ph.D. candidate in Electrical
and Computing Engineering at USC Viterbi, and Professor Yalda T. Uhls
of UCLA's Department of Psychology. They discovered many interesting connections between the portrayals of risky behaviors.

"There seems to be a correlation in the amount of content in a typical
film focused on substance abuse and the amount of sexual content. Whether intentionally or not, filmmakers seem to match the level of substance
abuse- related content with sexually explicit content," said Martinez.

Another interesting pattern also emerged. "We found that filmmakers
compensate for low levels of violence with joint portrayals of substance
abuse and sexual content," Martinez said.

Moreover, while many movies contain depictions of rampant drug-abuse and
sexual content, the researchers found it highly unlikely for a film to
have high levels of all three risky behaviors, perhaps because of Motion Picture Association (MPA) standards.



==========================================================================
They also found an interesting connection between risk behaviors and
MPA ratings. As sexual content increases, the MPA appears to put less
emphasis on violence/substance-abuse content. Thus, regardless of violent
and substance abuse content, a movie with a lot of sexual content will
likely receive an R rating.

Narayanan whose SAIL lab has pioneered the field of media informatics
and applied natural language processing in order to bring awareness in
the creative community about the nuances of storytelling, calls media "a
rich avenue for studying human communication, interaction and behavior,
since it provides a window into society." "At SAIL, we are designing technologies and tools, based on AI, for all stakeholders in this creative business -- the writers, film-makers and producers -- to raise awareness
about the varied important details associated in telling their story on
film," Narayanan said.

"Not only are we interested in the perspective of the storytellers of
the narratives they weave," Narayanan said, "but also in understanding
the impact on the audience and the 'take-away' from the whole
experience. Tools like these will help raise societally-meaningful
awareness, for example, through identifying negative stereotypes."
Added Martinez: "In the future, I'm interested in studying minorities
and how they are represented, particularly in cases of violence, sex
and drugs."

========================================================================== Story Source: Materials provided by
University_of_Southern_California. Note: Content may be edited for style
and length.


==========================================================================


Link to news story: https://www.sciencedaily.com/releases/2020/11/201117144539.htm

--- up 12 weeks, 1 day, 7 hours, 50 minutes
* Origin: -=> Castle Rock BBS <=- Now Husky HPT Powered! (1337:3/111)