In the annual Eurovision Song Contest, it is well known that countries often tend to be generous in their votes for the songs of their neighbours. This article looks at the evidence for this in the 2021 competition, and uses it as an opportunity to illustrate the technique of “bootstrapping” to assess statistical significance.
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Many British buildings are adorned with plaques, marking the birthplace or residence of a famous person, or the site of a significant event. Details of these plaques are available in an online database, and I thought it would be interesting to see how many of them have a musical connection.
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I recently stumbled across this page on Wikipedia, listing music students and their teachers. This is an ideal dataset to explore as a network diagram, or “graph”, in which a set of points (or “nodes”) are connected by lines (or “edges”). Here, the nodes are individuals, and there is an edge between them if one taught the other.
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In this previous post in the series, we used capitalisation to identify proper nouns (names, places, etc) in our dataset of song lyrics. Other parts of speech – verbs, adjectives, etc – are not so easy to identify, although software exists to do just that.
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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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