DESCRIPTION: ACTIV-ES is a comparable Spanish corpus comprised of film dialogue from Argentine, Mexican and Spanish productions. Titles for each of these three countries were seeded from the Internet Movie Database, subtitle data for the hearing impaired was provided by Opensubtitles.org and was post-processed to correct/remove subtitle, OCR and diacritic artifacts and annotated for part-of-speech.The data is available in two main formats: 1) running text for each document and 2) 1:5 gram aggregate files. Each format includes a plain text and part-of-speech annotated version. Document names reflect the language code, country, year, title, type, genre (first genre listed in the IMDb), and IMDb ID.For more information about the development and evaluation of these resources and to cite this work refer to:Francom, J., Hulden, M. and Ussishkin, A.. (2014) ACTIV-ES: a comparable, cross-dialect corpus of 'everyday' Spanish from Argentina, Mexico, and Spain. In Proceedings of the Ninth Annual Language Resources and Evaluation Conference, Reykjavik, Iceland. European Language Resources Association (ELRA).In version .02 of the tagged running format corpus in the /eagles directory has been added which includes the EAGLES tagset. This tagset is much more fleshed out than the simplified tagset in the /tagged directory. For information on the tagset refer here: http://nlp.lsi.upc.edu/freeling/doc/tagsets/tagset-es.html.

The dataset is from our recent study titled "Using data science to understand the film industry’s gender gap". To construct this dataset, we fused data from the online movie database IMDb with a dataset of movie dialogue subtitles to create the largest available corpus of movie social networks (15,540 networks).

More details on our research can be found at the following links: * Kagan, Dima, Thomas Chesney, and Michael Fire "Using data science to understand the film industry's gender gap." Nature Humanities and Social Sciences Communications, 6.1 (2020): 1-16 [Link] * "What do movie characters’ relationships reveal about gender, and how has this changed over time?", On Society Blog Post * Project's GitHub page * Our lab's website

Data Source: https://www.kaggle.com/datasets/gufukuro/movie-scripts-corpus Data Description : Movie Scripts Corpus This corpus was collected to use for screenplay analysis with machine learning methods. Corpus includes movie scripts, crawled from different sources, their annotations by script structural elements and movies metadata. Corpus description Screenplay data consists of: Movie scripts TXT-documents with raw full text (2858 docs) Movie scripts TXT-documents with full text lemmas (2858 docs) Manual annotation TXT-documents for some movie scripts (33 docs, more than 6000 annotated rows) Movie scripts annotations TXT-documents obtained by BERT Movie scripts annotations json-documents obtained by rule-based annotator ScreenPy Movies metadata consists of: Cut versions of movie reviews and scores from metacritic: Number of reviews: 21025 Number of movies with reviews: 2038 Metadata for movies, including: title, akas, launch year, score from metacritic, imdb user rating and number of votes from imdb.com, movie awards, opening weekend, producers, budget, script department, production companies, writers, directors, cast info, countries involved in production, age restrict, plot (with outline), keywords, genres, taglines, critics' synopsis Screenplay awards information: Academy Awards adapted screenplay, Academy Awards original screenplay, BAFTA, Golden Globe Award for Best Screenplay, Writers Guild Awards Winners & Nominees 2020-2013 nominations information for 462 movies in total. Movie characters data consists of: Script text fragments with dialogs and scene descriptions for characters, gathered with annotators: 2153 movies and text fragments for 32114 characters in total Gender labels for 4792 characters

Dataset Card for "imdb"

  Dataset Summary

Large Movie Review Dataset. This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training, and 25,000 for testing. There is additional unlabeled data for use as well.

  Supported Tasks and Leaderboards

More Information Needed

  Languages

More Information Needed… See the full description on the dataset page: https://huggingface.co/datasets/stanfordnlp/imdb.