Mining Game of Thrones Scripts with R by Gokhan Ciflikli
If you are serious about defeating all comers to Game of Thrones trivia, then you need to know the scripts cold. (sorry)
Ciflikli introduces you to the quanteda and analysis of the Game of Thrones scripts in a single post saying:
I meant to showcase the quanteda package in my previous post on the Weinstein Effect but had to switch to tidytext at the last minute. Today I will make good on that promise. quanteda is developed by Ken Benoit and maintained by Kohei Watanabe – go LSE! On that note, the first 2018 LondonR meeting will be taking place at the LSE on January 16, so do drop by if you happen to be around. quanteda v1.0 will be unveiled there as well.
Given that I have already used the data I had in mind, I have been trying to identify another interesting (and hopefully less depressing) dataset for this particular calling. Then it snowed in London, and the dire consequences of this supernatural phenomenon were covered extensively by the r/CasualUK/. One thing led to another, and before you know it I was analysing Game of Thrones scripts:
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2018, with its mid-term congressional elections, will be a big year for leaked emails, documents, in addition to the usual follies of government.
Text mining/analysis skills you gain with the Game of Thrones scripts will be in high demand by partisans, investigators, prosecutors, just about anyone you can name.
From the quanteda documentation site:
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quanteda is principally designed to allow users a fast and convenient method to go from a corpus of texts to a selected matrix of documents by features, after defining what the documents and features. The package makes it easy to redefine documents, for instance by splitting them into sentences or paragraphs, or by tags, as well as to group them into larger documents by document variables, or to subset them based on logical conditions or combinations of document variables. The package also implements common NLP feature selection functions, such as removing stopwords and stemming in numerous languages, selecting words found in dictionaries, treating words as equivalent based on a user-defined “thesaurus”, and trimming and weighting features based on document frequency, feature frequency, and related measures such as tf-idf.
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Once you follow the analysis of the Game of Thrones scripts, what other texts or features of quanteda will catch your eye?
Enjoy!