However, we do capture some information about the phrases, namely that both of them refer to a "dog" and to a "man". For example, the phrases "dog bites man" and "man bites dog" would end up with the same representation, despite referring to very different events.
This approach obviously loses some information about the document being analyses. In other words we throw away all information about the relative ordering of words. In what follows, I'm going to explore more general ways we can looking at natural language, focusing on the "bag-of-words" model.Ī "bag-of-words" model is one where we only care about the frequencies with which each word appears in the text of a document. While this is an interesting way to represent the bands, it is limited in what it captures. It also suggests that bands that swear more seem to use more complex words. Ploting one against the other we get the following:Īs you can see, Five Finger Death Punch have the highest number of swear words in their lyrics, and Pig Destroyer have the most complex wordplay. For example, taking 100 of the more popular band from the dataset, we look at all the lyrics for the band and ask what fraction of the words are swear words? We can also look what the readability of their lyrics, giving us a measure of how complex the language used is (where complexity is defined in terms of the number of syllables each word has). Once we have the data, there are a huge number of ways to represent it in numerical form. If people are interested I may release some n-gram data of the corpus. I collected the lyrics for my own entertainment, and it would be too easy for someone to use this data to copy darklyrics.
DEATHMETAL BANDS BY STATE CODE
After cleaning the data up, identifying the languages and splitting albums into songs, we are left with a dataset containing lyrics to 222,623 songs from 7,364 bands spread over 22,314 albums.īefore anyone asks, I have no intention of releasing either the raw lyric files or the code used to scrape the website. To get the lyrics, I scraped While darklyrics doesn't have a robots.txt file, I tried to be gentle with my requests. I haven't had much experience playing with natural language, so I decided to try out a few techniques on a dataset I scraped from the internet: a set of heavy metal lyrics (and associated genres). Once in this form, statistical or machine learning approaches can be leveraged to solve a whole range of problems. They tend to start by making simplifying assumptions about the data, and then using these assumptions convert the raw text into a more quantitative structure, like vectors or graphs. That said, there are an increasing number of techniques that have been developed to provide some insight into natural language. In the face of this complexity it is not surprising that understanding natural language, in the same way humans do, with computers is still a unsolved problem. These variations and versatility of natural language are the reason that it is so powerful as a way to communicate and share ideas. Not only is the space of possible strings huge, but the interpretation of a small sections of a document can take on vastly different meanings depending on what context surround it.
Natural language is made up of sequences of discrete characters arranged into hierarchical groupings: words, sentences and documents, each with both syntactic structure and semantic meaning.
It is also quite unlike any sort of data I have worked with before. It is all around us, and the rate at which it is produced in written, stored form is only increasing. I call these band "Metal" here for the sake of brevity only, and I apologise in advance. I know some people have strong feelings about how genres are defined, and would probably disagree with me about some of the bands I call metal in this post. In this post I refer to lyrics of certain bands as being "Metal". Most of the code used in these posts is available here. Edit: Part 2 of this post can be found here, and part 3 here.
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