A (Proper) Statistical analysis of the prose works of Samuel Beckett

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Content warning: If you want to get to the fun parts, the results of an analysis of Beckett’s use of language, skip to sections VII and VIII. Everything before that is navel-gazing methodology stuff.

If you want to know how I carried out my analysis, and utilise my code for your own purposes, here’s a link to my R code on my blog, with step-by-step instructions, because not enough places on the internet include that.

I: Things Wrong with my Dissertation’s Methodology

For my masters, I wrote a 20000 word dissertation, which took as its subject, an empirical analysis of the works of Samuel Beckett. I had a corpus of his entire works with the exception of his first novel Dream of Fair to Middling Women, which is a forgivable lapse, because he ended up cannibalising it for his collection of short stories, More Pricks than Kicks.

Quantitative literary analysis is generally carried out in one of two ways, through either one of the open-source programming languages Python or R. The former you’ve more likely to have heard of, being one of the few languages designed with usability in mind. The latter, R, would be more familiar to specialists, or people who work in the social sciences, as it is more obtuse than Python, doesn’t have many language cousins and has a very unfriendly learning curve. But I am attracted to difficulty, so I am using it for my PhD analysis.

I had about four months to carry out my analysis, so the idea of taking on a programming language in a self-directed learning environment was not feasible, particularly since I wanted to make a good go at the extensive body of secondary literature written on Beckett. I therefore made use of a corpus analysis tool called Voyant. This was a couple of years ago, so this was before its beta release, when it got all tricked out with some qualitative tools and a shiny new interface, which would have been helpful. Ah well. It can be run out of any browser, if you feel like giving it a look.

My analysis was also chronological, in that it looked at changes in Beckett’s use of language over time, with a view to proving the hypothesis that he used a less wide vocabulary as his career continued, in pursuit of his famed aesthetic of nothingness or deprivation. As I wanted to chart developments in his prose over time, I dated the composition of each text, and built a corpus for each year, from 1930–1987, excluding of course, years in which he just wrote drama, poetry, which wouldn’t be helpful to quantify in conjunction with one another. Which didn’t stop me doing so for my masters analysis. It was a disaster.

II: Uniqueness

Uniqueness, the measurement used to quantify the general spread of Beckett’s vocabulary, was obtained by the generally accepted formula below:

unique word tokens / total words

There is a problem with this measurement, in that it takes no account of a text’s relative length. As a text gets longer, the likelihood of each word being used approaches 1. Therefore, a text gets less unique as it gets bigger. I have the correlations to prove it:

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There have been various solutions proposed to this quandary, which stymies our comparative analyses, somewhat. One among them is the use of vectorised measurements, which plot the text’s declining uniqueness against its word count, so we see a more impressionistic graph, such as this one, which should allow us to compare the word counts for James Joyce’s novels, A Portrait of the Artist as a Young Man and his short story collection, Dubliners.

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All well and good for two or maybe even five texts, but one can see how, with large scale corpora, this sort of thing can get very incoherent very quickly. Furthermore, if one was to examine the numbers on the y-axis, one can see that the differences here are tiny. This is another idiosyncrasy of stylostatistical methods; because of the way syntax works, the margins of difference wouldn’t be regarded as significant by most statisticians. These issues relating to the measurement are exacerbated by the fact that ‘particles,’ the atomic structures of literary speech, (it, is, the, a, an, and, said, etc.) make up most of a text. In pursuit of greater statistical significance for their papers, digital literary critics remove these particles from their texts, which is another unforgivable that we do anyway. I did not, because I was concerned that I was complicit in the neoliberalisation of higher education. I also wrote a 4000 word chapter that outlined why what I was doing was awful.

IV: Ambiguity

The formula for ambiguity was arrived at by the following formula:

number of indefinite pronouns/total word count

I derived this measurement from Dr. Ian Lancashire’s study of the works of Agatha Christie, and counted Beckett’s use of a set of indefinite pronouns, ‘everyone,’ ‘everybody,’ ‘everywhere,’ ‘everything,’ ‘someone,’ ‘somebody,’ ‘somewhere,’ ‘something,’ ‘anyone,’ ‘anybody,’ ‘anywhere,’ ‘anything,’ ‘no one,’ ‘nobody,’ ‘nowhere,’ and ‘nothing.’ Those of you who know that there are more indefinite pronouns than just these, you are correct, I had found an incomplete list of indefinite pronouns, and I assumed that that was all. This is just one of the many things wrong with my study. My theory was that there were to be correlations to be detected in Beckett’s decreasing vocabulary, and increasing deployment of indefinite pronouns, relative to the total word count. I called the vocabulary measure ‘uniqueness,’ and the indefinite pronouns measure I called ‘ambiguity.’ This in tenuous I know, indefinite pronouns advance information as they elide the provision of information. It is, like so much else in the quantitative analysis of literature, totally unforgivable, yet we do it anyway.

V: Hapax Richness

I initially wanted to take into account another phenomenon known as the hapax score, which charts occurrences of words that appear only once in a text or corpus. The formula to obtain it would be the following:

number of words that appear once/total word count

I believe that the hapax count would be of significance to a Beckett analysis because of the points at which his normally incompetent narrators have sudden bursts of loquaciousness, like when Molloy says something like ‘digital emunction and the peripatetic piss,’ before lapsing back into his ‘normal’ tone of voice. Once again, because I was often working with a pen and paper, this became impossible, but now that I know how to code, I plan to go over my masters analysis, and do it properly. The hapax score will form a part of this new analysis.

VI: Code & Software

A much more accurate way of analysing vocabulary, for the purposes of comparative analysis when your texts are of different lengths, therefore, would be to randomly sample it. Obviously not very easy when you’re working with a corpus analysis tool online, but far more straightforward when working through a programming language. A formula for representative sampling was found, and integrated into the code. My script is essentially a series of nested loops and if/else statements, that randomly and sequentially sample a text, calculate the uniqueness, indefiniteness and hapax density ten times, store the results in a variable, and then calculate the mean value for each by dividing the result by ten, the number of times that the first loop runs. I inputted each value into the statistical analysis program SPSS, because it makes pretty graphs with less effort than R requires.

VII: Results

I used SPSS’ box plot function first to identify any outliers for uniqueness, hapax density and ambiguity. 1981 was the only year which scored particularly high for relative usage of indefinite pronouns.

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It should be said that this measure too, is correlated to the length of the text, which only stands to reason; as a text gets longer the relative incidence of a particular set of words will decrease. Therefore, as the only texts Beckett wrote this year, ‘The Way’ and ‘Ceiling,’ both add up to about 582 words (the fifth lowest year for prose output in his life), one would expect indefiniteness to be somewhat higher in comparison to other years. However, this doesn’t wholly account for its status as an outlier value. Towards the end of his life Beckett wrote increasingly short prose pieces. Comment C’est (How It Is) was his last novel, and was written almost thirty years before he died. This probably has a lot to do with his concentration on writing and directing his plays, but in his letters he attributed it to a failure to progress beyond the third novel in his so-called trilogy of Molloy, Malone meurt (Malone Dies) and L’innomable (The Unnamable). It is in the year 1950, the year in which L’inno was completed, that Beckett began writing the Textes pour rien (Texts for Nothing), scrappy, disjointed pieces, many of which seem to be taking up from where L’inno left off, similarly the Fizzlesand the Faux Départs. ‘The Way,’ I think, is an outgrowth of a later phase in Beckett’s prose writing, which dispenses the peripatetic loquaciousness and the understated lyricism of the trilogy and replaces it with a more brute and staccato syntax, one which is often dependent on the repetition of monosyllables:

No knowledge of where gone from. Nor of how. Nor of whom. None of whence come to. Partly to. Nor of how. Nor of whom. None of anything. Save dimly of having come to. Partly to. With dread of being again. Partly again. Somewhere again. Somehow again. Someone again.

Note also the prevalence of particle words, that will have been stripped out for the analysis, and the ways in which words with a ‘some’ prefix are repeated as a sort of refrain. This essential structure persists in the work, or at least the artefact of the work that the code produces, and hence of it, the outlier that it is.

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From plotting all the values together at once, we can see that uniqueness is partially dependent on hapax density; the words that appear only once in a particular corpus would be important in driving up the score for uniqueness. While there could said to be a case for the hypothesis that Beckett’s texts get less unique, more ambiguous up until 1944, when he completed his novel Watt, and if we’re feeling particularly risky, up until 1960 when Comment C’est was completed, it would be wholly disingenuous to advance it beyond this point, when his style becomes far too erratic to categorise definitively. Comment C’est is Beckett’s most uncompromising prose work. It has no punctuation, no capitalisation, and narrates the story of two characters, in a kind of love, who communicate with one another by banging kitchen implements off another:

as it comes bits and scraps all sorts not so many and to conclude happy end cut thrust DO YOU LOVE ME no or nails armpit and little song to conclude happy end of part two leaving only part three and last the day comes I come to the day Bom comes YOU BOM me Bom ME BOM you Bom we Bom

VIII: Conclusion

I would love to say that the general tone is what my model is being attentive to, which is why it identified Watt and How It Is as nadirs in Beckett’s career but I think their presence on the chart is more a product of their relative length, as novels, versus the shorter pieces which he moved towards in his later career. Clearly, Beckett’s decision to write shorter texts, make this means of summing up his oeuvre in general, insufficient. Whatever changes Beckett made to his aesthetic over time, we might not need to have such complicated graphs to map, and I could have just used a word processor to find it — length. Bom and Pim aside, for whatever reason after having written L’inno none of Beckett’s creatures presented themselves to him in novelistic form again. The partiality of vision and modal tone which pervades the post-L’inno works demonstrates, I think far more effectively what is was that Beckett was ‘pitching’ for, a new conceptual aspect to his prose, which re-emphasised its bibliographic aspects, the most fundamental of which was their brevity, or the appearance of an incompleteness, by virtue of being honed to sometimes less than five hundred words.

The quantification of differing categories of words seems like a radical, and the most fun, thing to quantify in the analysis of literary texts, as the words are what we came for, but the problem is similar to one that overtakes one who attempts to read a literary text word by word by word, and unpack its significance as one goes: overdetermination. Words are kaleidoscopic, and the longer you look at them, the more threatening their darkbloom becomes, the more they swallow, excrete, the more alive they are, all round. Which is fine. Letting new things into your life is what it should be about, until their attendant drawbacks become clear, and you start to become ambivalent about all the fat and living things you have in your head. You start to wish you read poems instead, rather than novels, which make you go mad, and worse, start to write them. The point is words breed words, and their connections are too easily traced by computer. There’s something else about knowing that their exact correlations to a decimal point. They seem so obvious now.

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