{"id":1440,"date":"2012-02-06T18:13:37","date_gmt":"2012-02-06T23:13:37","guid":{"rendered":"http:\/\/winedarksea.org\/?p=1440"},"modified":"2025-02-10T17:43:49","modified_gmt":"2025-02-10T22:43:49","slug":"the-very-strange-language-of-a-midsummer-nights-dream","status":"publish","type":"post","link":"https:\/\/winedarksea.org\/?p=1440","title":{"rendered":"The very strange language of A Midsummer Night\u2019s Dream"},"content":{"rendered":"

I just got back from a fun and very educative trip to Shakespeare\u2019s Globe<\/a> in London, hosted by Dr Farah Karim-Cooper, who is director of research<\/a> there.<\/p>\n

The Globe stages an annual production aimed at schools (45,000 free tickets have been distributed over the past five years), and this year\u2019s play is A Midsummer Night\u2019s Dream<\/em>. I was invited down to discuss the language of the play with the cast and crew as they begin rehearsals.<\/p>\n

This was a fascinating opportunity for me to test our visualisation tools and analysis on a non-academic audience – and the discussions I had with the actors opened my eyes to applications of the tools we haven\u2019t considered before. They also came up with a series of sharp observations about the language of the play in response to the linguistic analysis.<\/p>\n

I began with a tool developed by Martin Mueller\u2019s team at Northwestern University: Wordhoard<\/a>,\u00a0as a way of getting a quick overview of the lexical patterns in the play, and introducing people to thinking statistically about language.<\/p>\n

Here\u2019s the wordcloud Wordhoard generates for a loglikelihood<\/a>\u00a0analysis of MSND<\/em> compared with the whole Shakespeare corpus:<\/p>\n

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Loglikelihood takes the frequencies of words in one text (in this case MSND<\/em>) and compares them with the frequencies of words in a comparison, or reference, sample (in this case, the whole Shakespeare corpus). It identifies the words that are used significantly more or less frequently in the analysis text than would be expected given the frequencies found in the comparison sample. In the wordcloud, the size of a word indicates how strongly its frequency departs from the expected. Words in black appear more<\/em> frequently than we would expect, and words in grey appear less<\/em> frequently.<\/p>\n

As is generally the case with loglikelihood tests, the words showing the most powerful effects here are nouns associated with significant plot elements: \u2018fairy\u2019, \u2018wall\u2019, \u2018moon\u2019, \u2018lion\u2019 etc. If you\u2019ve read the play, it is not hard to explain why these words are used in MSND<\/em> more than in the rest of Shakespeare – and you really don\u2019t need a computer, or complex statistics, to tell you that. To paraphrase Basil Fawlty, so far, so bleeding obvious.<\/p>\n

Where loglikelihood results normally get more interesting – or puzzling – is in results for function words<\/a><\/em> (pronouns, auxiliary verbs, prepositions, conjunctions) and in those words that are significantly less<\/em> frequent than you\u2019d expect.<\/p>\n

Here we can see some surprising results: why does Shakespeare use \u2018through\u2019 far more frequently in this play than elsewhere? Why are the masculine pronouns \u2018he\u2019 and \u2018his\u2019 used less frequently? (And is this linked to the low use of \u2018lord\u2019?) Why is \u2018it\u2019 rare in the play? And \u2018they\u2019 and \u2018who\u2019 and ‘of’?<\/p>\n

At this stage we started to look at our results from Docuscope for the play, visualised using Anupam Basu\u2019s LATtice<\/a>.<\/p>\n

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The heatmap shows all of the folio plays compared to each other: the darker a square is, the more similar the plays are linguistically. The diagonal of black squares running from bottom left to top right marks the points in the map where plays are \u2018compared\u2019 to themselves: the black indicates identity. Plays are arranged up the left hand side of the square in ascending chronological order from Comedy of Errors<\/em> at the bottom to Henry VIII<\/em> at the top – the sequence then repeats across the top from left to right – so the black square at the bottom left is Comedy of Errors<\/em> compared to itself, while the black square at the top right is Henry VIII<\/em>.<\/p>\n

One of the first things we noticed when Anupam produced this heatmap was the two plays which stand out as being unlike almost all of the others, producing four distinct light lines which divide the square of the map almost into nine equal smaller squares:<\/p>\n

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These two anomalous plays are Merry Wives of Windsor<\/em> (here outlined in blue) and A Midsummer Night\u2019s Dream<\/em> (yellow). It is not so surprising to find Wives<\/em> standing out, given the frequent critical observation that this play is generically and linguistically unusual for Shakespeare: but A Midsummer Night\u2019s Dream<\/em> is a result we certainly would not have predicted.<\/p>\n

This visualisation of difference certainly caught the actors\u2019 attention, and they immediately focussed in on the very white square about 2\/3 of the way along the MSND<\/em> line (here picked out in yellow):<\/p>\n

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So which play is MSND<\/em> even less like than all of the others? A tragedy? A history? Again, the answer is not one we\u2019d have guessed: Measure for Measure<\/em>.<\/p>\n

This is a good example of how a visualisation can alert you to a surprising finding. We would never have intuited that MSND<\/em> was anomalous linguistically without this heatmap. It is also a good example of how visualisations should send you back to the data: we now need to investigate the language of MSND<\/em> to explain what it is that Shakespeare does, or does not do, in this play that makes it stand out so clearly. The visualisation is striking – and it allowed the cast members to identify an interesting problem very quickly – but the visualisation doesn\u2019t give us an explanation for the result. For that we need to dig a bit deeper.<\/p>\n

One of the most useful features of LATtice is the bottom right window, which identifies the LATs that account for the most distance between two texts:<\/p>\n

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This is a very quick way of finding out what is going on – and here the results point us to two LATs which are much more frequent in MSND<\/em> than Measure for Measure<\/em>: SenseObject and SenseProperty. SenseObject picks up concrete nouns, while SenseProperty codes for adjectives describing their properties. A quick trip to the LATice box plot screen (on the left of these windows):<\/p>\n

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confirms that MSND<\/em> (red dots) is right at the top end of the Shakespeare canon for these LATs (another surprise, since we\u2019ve got used to thinking of these LATs as characteristic of History), while Measure for Measure<\/em>\u00a0(blue dots) has the lowest rates in Shakespeare for these LATs.<\/p>\n

So Docuscope findings suggest that MSND<\/em> is a play concerned with concrete objects and their descriptions – another counter-intuitive finding given the associations most of us have with the supposed ethereal, fairy, dream-like atmosphere of the play. Cast members were fascinated by this and its possible implications for how they should use props – and someone also pointed out that many of the names in the play are concrete nouns (Quince, Bottom, Flute, Snout, Peaseblossom, Cobweb, Mote and so on) – what is the effect on the audience of this constant linguistic wash of \u2018things\u2019?<\/p>\n

Here is a screenshot from Docuscope with SenseObject and SenseProperty tokens underlined in yellow. Reading these tokens in context, you realise that many of these concrete objects and qualities, in this section at least, are fictional in the world of the play. A wall is evoked \u2013 but it is one in a play, represented by a man. Despite the frequency of SenseObject in this play, we should be wary of assuming that this implies the straightforward evocation of a concrete reality (try clicking if you need to enlarge):<\/p>\n

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Also raised in MSND<\/em> are LATs to do with locating and describing space: Motions and SpaceRelations (as suggested by our loglikelihood finding for \u2018through\u2019?). So accompanying a focus on things, is a focus on describing location, and movement – perhaps, someone suggested, because the characters are often so unsure of their location? (In the following screenshot, Motions and SpatialRelation tokens are underlined in yellow.)<\/p>\n

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Moving on, we also looked at those LATs that are relatively absent from MSND<\/em> – and here the findings were very interesting indeed. We have seen that MSND<\/em> does not pattern like a comedy – and the main reason for this is that it lacks the highly interactive language we expect in Shakespearean comedy: DirectAddress and Question are lowered. So too are PersonPronoun (which picks up third person pronouns, and matches our loglikelihood finding for \u2018he\u2019 and \u2018his\u2019), and FirstPerson – indeed, all<\/em> types of pronoun are less frequent in the play than is normal for Shakespeare. At this point one of the actors suggested that the lack of pronouns might be because full names are used constantly – she\u2019d noticed in rehearsal how often she was using characters\u2019 names \u2013 and we wondered if this was because the play\u2019s characters are so frequently uncertain of their own, and others\u2019 identity.<\/p>\n

Also lowered in the play is PersonProperty, the LAT which picks up familial roles (\u2018father\u2019, \u2018mother\u2019, \u2018sister\u2019 etc) and social ones (job titles) – if you add this to the lowered rate of pronouns, then a rather strange social world starts to emerge, one lacking the normal points of orientation (and the play is also low on CommonAuthority, which picks up appeals to external structures of social authority \u2013 the law, God, and so on).<\/p>\n

The visualisation, and Docuscope screens, provoked a discussion I found fascinating: we agreed that the action of the play seems to exist in an eternal present. There seems to be little sense of future or past (appropriately for a dream) – and this ties in with the relative absence of LATs coding for past tense and looking back. As the LATtice heatmap first indicated, MSND<\/em> is unlike any of the recognised Shakespearean genres – but digging into the data shows that it is unlike them in different ways:<\/p>\n