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Difference between revisions of "Movie Project"

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(Interested)
(Interested)
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* Xiao (Thomas) Zhang
 
* Xiao (Thomas) Zhang
 
* [[Lu Liu]]
 
* [[Lu Liu]]
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* Harrison Smith
  
 
== Network Construction and Time Dynamics ==
 
== Network Construction and Time Dynamics ==

Revision as of 21:09, 17 June 2016

Predicting Metadata from Network Structure

Summary

This is a 'meta' task... Essentially the idea is to use machine learning or any kind of other techniques to predict things like success, genre etc. of a movie.

First Few Tasks

  • Script to download all movie galaxies
  • Conversion from gephi to useful format (gml?)
  • Network comparison (Przulj code?)


Interested

  • Michael Schaub
  • Andrew Meller
  • Xiao (Thomas) Zhang
  • Lu Liu
  • Harrison Smith

Network Construction and Time Dynamics

Summary

The main goal here will be to look at the time dynamics of the movie character networks, with a particular focus on how characters are introduced to the network. We can use this analysis to see how stories develop through the network construction. This can be compared between movies to see how similar network construction and dynamics are across movies.

Interested

  • Moriah Echlin (moriah.echlin@gmail.com)
  • Dan Biro (daniel.biro@med.einstein.yu.edu)

Trope network

Summary

There is another dataset from TV Tropes (http://tvtropes.org) that I would be happy to bring into this project. Tropes are story telling elements (if you go to http://tvtropes.org/pmwiki/pmwiki.php/Main/Tropes and read a few entries, you will quickly get a sense of them). The dataset contains ~3,500 movies and a list of tropes for each, as well as the movie's year, IMDB rating, and box office.

I am interested in studying story archetypes (typical plots). From a network perspective, it may be possible to build a directed network of "narrative" tropes (identified in http://tvtropes.org/pmwiki/pmwiki.php/Main/NarrativeTropes , but may need more inspection), where the edge directions represent time orders. The time sequence of tropes is not represented in the TV Tropes data, therefore I'm thinking if any of Will's datasets may shed some lights on it. If the network construction is successful, extracting the backbones of the network will show us what are the most commonly used story arcs in movies, etc.

This is only a half-baked idea, and I would love to hear any ideas/comments. If anyone is interested, please let me(Elise) know.

Interested

Yizhi (Elise) Jing (jingy@indiana.edu)


Natural Language Processing of Dialogues

Summary

Interested

  • Marius Somveille (marius.somveille@zoo.ox.ac.uk)
  • Lu Liu