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These are all counts, and the difference between a type and a token is that the former provides a count of distinct tokens: (a, b, c, c) is four tokens but three types. When you call summary on a corpus, it reports descriptives on type, tokens, and sentences. quanteda readily offers several statistics that lend themselves very well to Joy plots. Numbers and Greek letters are cool, however you’ll find that a well-made graph can convey a lot at a glance. Got.LDA <- LDA(convert(got.dfm, to = "topicmodels"),
WRITE IN GAME OF THRONES FONT CODE
The below code is not evaluated here, but if you do, you’ll find that GoT consistently revolves around lords, kings, the realm, men, and fathers with the occasional khaleesi thrown in. 2 We’ll need topicmodels, and might as well write another for-loop (double-trouble). We will model topic similarities and call it a package. # white deserter detail corners guardsman pups # protector kingdoms robert honor hundred shadowcats Using textstat_simil, we can get the top n words that are associated with it: sim <- textstat_simil(got.dfm, diag = TRUE, c("throne", "realm", "walkers"), Yet another thing we can calculate is term similarity and distance. You can refer to the titles object to see the raw counts rather than column percentages by row. This particular exercise would have definitely benefited from S7 scripts. # View got.corpus 40%) called/mentioned by her actual name. # Attaching package: 'quanteda' # The following object is masked from 'package:utils': Yet, we can manipulate the meta data manually as well: library(quanteda) # quanteda version 0.99.22 # Using 7 of 8 threads for parallel computing # The showmeta argument should cut off the additional information you get at the end of a summary, however it doesn’t work on my computer. Which is great, otherwise I don’t think I’d have gotten into it! Let’s transform our scripts dataset into a corpus. That should be doable - quanteda offers a smooth ride and it has a nicely documented website. Right, let’s generate some numbers to go along with all this text.Īs I covered n-grams in my previous post, I will try to diversify a bit. You can easily fix those with a string replacement solution I’ll let them be. One quirk of this website is that they seem to have used small case L for capital I (e.g. Those are the first sentences of the first ten GoT episodes - looks good! We won’t worry about the backslash on line 7 for now. Look at me! Do you remember me now, boy, eh? Remember me? Ther Another visit? lt seems you're my last fr # 7 "\"Summoned to court to answer for the crimes \"of your bannerman Gregor Cl I would rise, but Do you know what your wife has # 5 "Does Ser Hugh have any family in the capital? No. # 4 "The little lord's been dreaming again. Grand Maester Pycelle has called a meeting of the Sma Url % html_node(".scrolling-script-container")įull.text % html_node(".scrolling-script-container") This is not a general rule, so you’ll probably have to do this every time you try to scrape a new website. If you hover where the text is located in inspection mode, you’ll find that it’s wrapped in ‘scrolling-script-container’ tags. With any modern browser, you should be able to inspect the page to see the underlying code. Cool, let’s fire up the very first episode. Mighty Google told me that this website has GoT scripts online. How it works is that you feed it a URL, it reads the html, you locate which html tag/class contains the information you want to extract, and finally it lets you clean up the text by removing the html bits. rvest package is especially convenient to use. Nowadays it’s really easy to scrape interesting stuff online. Wear it like armor, and it can never be used to hurt you.’ It’s probably a Chinese proverb. A wise man once quipped: ‘Never forget what you are.
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I decided to go with the show because I’m a filthy casual fan. With GoT, there are two obvious avenues: full-text books or the show scripts. I intend to keep to the organic three-step structure I have developed lately in my posts: obtaining data, showcasing a package, and visualising the end result.
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Mandatory spoilers tag, the rest of the post contains (surprise) spoilers (although only up until the end of the sixth season).