This is a story about how we got here. We were blown away and eagerly waited for someone to replicate it for other genres. One year later, we were still waiting. Inwe ranked rappers by the size of their vocabularyand this felt like the perfect sequel To start, we need a dataset that represents hip hop.
But what matters for our analysis is usage in hip hop compared to usage in all music lyrics. We want words unique to the genre: We used lyrics fromsongs about 47 million words spanning all music genres, except hip hop.
Dots farther to the right are more popular in hip hop. Dots farther up-top are popular in other bitcoin music rapper words. Words near the grey line are no more common in hip hop than anywhere else. At the extremes, some words have incredibly high odds of only appearing in hip hop. And some rarely appear in hip hop. In other genres it is was ranked Many of these words are slang, words that may not have existed pre-hip-hop or arguably they exist because hip hop.
There are some interesting non-slang entries, such as clique at For bitcoin music rapper words, what words are disproportionately used by Migos? We need to change approaches. But averages can be really misleading. A just says it more often. We started this essay with a map of lyrically bitcoin music rapper words rappers, and now you can see how it was drawn: Positioning each rapper next to his or her lyrically similar family is a difficult task.
Migos is most-similar to Gucci Mane. But Gucci Mane is most similar to Lil Wayne. Luckily, there's a technique called t-SNE, where a computer considers all these relationships and tries to place similar artists closer together. We can also see broader groupings, bitcoin music rapper words as region or era. This essay covers fairly advanced statistical concepts including machine bitcoin music rapper words You didn't even consider Which means bitcoin music rapper words get:.
Natural language processing, tf-idf, machine learning algorithms t-SNEand cosine similarity. The general music corpus was formed using data from LyricFind. We filtered hip-hop artists by cross-referencing their primary genre on MusixMatch.
For consistency, The hip hop data was cleaned using the same script as the LyricFind corpus. This included efforts bitcoin music rapper words standardize spelling, remove capitalization, and apply light lemmatization. For example, this is of appearences in hip hop corpus divided by total words in hip hop corpus.
We then compare that to the same math for the general corpus. Some words were filtered from this list that, while indexing high bitcoin music rapper words hip hop vs. These all had fewer than 1, occurances in the hip hop corpus. Each rapper gets assigned a tf-idf score for every word in the hip-hop corpus. We made two slight modifications to the traditional formula. You can read more about why you might want to do sublinear scaling here. Cosine similarity is a common way of calculating the similarity between two vectors by taking the cosine of the angle between them.
In our case, that means taking the tf-idf vector for bitcoin music rapper words artist and comparing it to that of another. Higher cosine values imply more similarity, with an upper bound of 1 when the vectors are perfectly similar. To create our map of rappers, we used a dimensionality reduction technique called t-SNE. We took the tf-idf matrix and first reduced it to bitcoin music rapper words dimensions using Truncated singular value decomposition SVD. We then took the resulting matrix and fed it into t-SNE with a perplexity parameter set to The output of the t-SNE algorithm mapped rappers to a two-dimensional space based on the similarity of their lyrics.
Special thanks to Josh Upton for edits. The Language of Hip Hop. What Makes a Word Central to an Artist? Mapping the Lyrical Similarity of Rappers.
Which means you get: Certificate of Completion I unintentionally read about fancy math and stats Things I may now have an opinion on: Methdology Notes The general music corpus was formed using data from LyricFind.
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