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A study of which movie plots tend to perform the best in terms of annual revenue. This experiment used sentiment analysis on in-depth plot descriptions from Wikipedia to construct and quantify the plot of the movie.

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Deriving the most profitable movie plot from data

By Abhinav Raghunathan

The work in this repository was implemented for an article written on Medium, inspired by a fascinating machine learning paper on plotlines by Andrew J. Reagan, Lewis Mitchell, Dilan Kiley, Christopher M. Danforth, and Peter Sheridan Dodds (2016).

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License

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Special thanks goes to the below sources for their data and the masterminds behind the R Programming Language and its assorted libraries.

Data Sources:

  1. Movie Metadata: Rounak Banik on Kaggle
  2. Movie Plots: JustinR on Kaggle

Relevant R Packages:

  1. Hadley Wickham (2017). tidyverse: Easily Install and Load the 'Tidyverse'. R package version 1.2.1. https://CRAN.R-project.org/package=tidyverse

  2. Hadley Wickham, Romain François, Lionel Henry and Kirill Müller (2020). dplyr: A Grammar of Data Manipulation. R package version 0.8.5. https://CRAN.R-project.org/package=dplyr

  3. H. Wickham. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.

  4. Jeffrey B. Arnold (2019). ggthemes: Extra Themes, Scales and Geoms for 'ggplot2'. R package version 4.2.0. https://CRAN.R-project.org/package=ggthemes

  5. Rinker, T. W. (2019). sentimentr: Calculate Text Polarity Sentiment version 2.7.1. http://github.com/trinker/sentimentr

  6. Lincoln A. Mullen et al., "Fast, Consistent Tokenization of Natural Language Text," Journal of Open Source Software 3, no. 23 (2018): 655, https://doi.org/10.21105/joss.00655.

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A study of which movie plots tend to perform the best in terms of annual revenue. This experiment used sentiment analysis on in-depth plot descriptions from Wikipedia to construct and quantify the plot of the movie.

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