Previously, we have looked at repetition in our dataset of song lyrics. This seventh article in the series considers a related issue – rhyming patterns. We are only interested here in the last word of each line – i.e. the string of characters between the last space and the end-of-line character \n.
This is the sixth in a series of articles looking at different ways of analysing a dataset of song lyrics. In this article we will be venturing into hyperspace to explore the differences and similarities between artists, in terms of the words they use in their songs.
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In a later post in this series of articles analysing a dataset of song lyrics, I will consider the more general question of identifying parts-of-speech (nouns, verbs, adjectives, etc.), which can greatly expand what can be learned from statistical text analysis. However, in this article, I will focus on a particular part of speech: proper nouns.
Continue reading →In this fourth article in the series looking at our song lyrics dataset, we will begin to consider the meaning of the lyrics, rather than just treating the words as abstract objects. A simple technique for quantifying the meaning of texts is known as ‘sentiment analysis’.
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We all know that a good song depends on repetition – both of the tune and the lyrics. Too much repetition and it is just boring; too little, and it can lack structure. This article looks at different aspects of repetition in song lyrics.
Continue reading →In the previous article in this series we looked at counting the frequency of words in a dataset of song lyrics. This time we will look at combinations of words – or n-grams.
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This is the first of a series of articles about analysing text data. The statistical music historian might be interested in many sorts of text – from lists and catalogues through to complex ‘free format’ writing in tweets, record reviews, composer biographies, or encyclopedias. For these articles I will consider a dataset of song lyrics, taken from the LyricWiki website [since I wrote this post, LyricWiki has disappeared, although there are several other sources of song lyrics that could be used].
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One of the things that seems to distinguish ‘classical’ from ‘popular’ music is the fact that the same classical composers and works can remain at the top for very long periods of time – decades, even centuries – whereas popular music songs and artists can reach the top of the charts, sell millions of records, and disappear within a matter of months. But is this difference real?
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The New York Philharmonic has got an excellent online archive of all of its concerts since 1842. This article uses the archive to investigate which composers tend to be programmed together.
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One of the annoying things about TV talent shows is the fact that the winning act very often seems to be the one that performed last. I thought I would check whether this was actually the case, using the Wikipedia data detailing all 15 series of the UK ‘X Factor’.
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