Over the last years, some developments paved the way for the computer-based analysis of dramatic texts. On the one hand, more and more texts are available electronically as, for example, in the collection Théâtre classique for French drama, the complete works of Shakespeare and a collection of German drama and libretti. On the other hand, we can now see the emergence of new methods and tools that allow us to gain and compute information like, for example, word frequency, speech length, configuration structures or topics automatically, also from large amounts of texts.
Only rarely, results of computer-based text analysis find their way into literary history (except for some studies on authorship attribution as, for example, in the Molière/Corneille and the Shakespeare/Marlowe debate). The books of, for instance, Franco Moretti, Matthew Jockers, Matt Erlin/Lynne Tatlock and Stephen Ramsay may serve as evidence that this is now changing but they do not deal with drama. As questions of literary criticism and history are at the heart of literary studies and dramatic texts have genuine textual structures, the workshop aimed at evaluating the possibilities of computer-based drama analysis for theses domains. What can be the use of the mostly quantitative data for questions such as style, popularity, canonicity, genre, periods of literary history, and individual authorial periods of production? What kind of new questions, which new micro- or macronarratives are arising from these approaches? Where are the differences to prior non-computational approaches to quantitative aspects of drama (see e.g. the works of F.v.Cube/W. Reichert, Solomon Marcus, Manfred Pfister)?
These were the underlying questions of the workshop on Computer-based analysis of drama and its uses for literary criticism and historiography, which took place on 12-13 March 2015 in Munich at the Bavarian Academy of Sciences and Humanities. It was a first inquiry in this emerging field with scholars from the disciplines of literary criticism, computer science and linguistics involved. I am gratful for the funding on behalf of the Young Scholars Program of the Bavarian Academy of Sciences and Humanities for this workhop.
In the beginning of the workshop Martin Mueller the TEI-expert and Co-Author of The Chicago Homer and WordHoard emphasized the necessity of corpus curation. In his paper Shakespeare His Contemporaries: A corpus-wide approach he showed that linguistic as well as semantic annotation are the prerequisites for later analysis of a corpus. On a corpus of 550 English plays written between 1576 and 1642 he showed not only the usefulness of linguistic annotation (like Tokenization, Lemmatization, POS-tagging, Standardization of Spelling, Division into N-grams) but also presented the idea of building a meta-castlist that associates each of the ~13,500 speaking roles with a corpus-wide ID and properties of sex, age, kinship, and social status. In developing the labels for social status together with undergraduates, he aims at understanding Shakespeares plays from a todays point of view. He also plans to compute who speaks to whom in verse or prose and at what length. For further reading see his Blog-entry.
Gerhard Heyer and Thomas Efer provided interesting insights in their Toolbox for Drama Mining (Information Theory and Structural Analysis: A Toolbox for Drama Mining). It allows to compute each of the following features, relate them to one another and visualize the results: block entropy, number of types, number of tokens, number of characters, speech length of characters (median and average), speech length complexity, token complexity and median and maximum length of monologues. Networks of characters are analyzed and visualized as well. Right now the parsing of dramatic texts is adapted for the Folger Shakespeare texts and for drama from the Gutenberg collection. The aim of their project is to gain insights in the linguistic structures and the development of the dramatic works of Shakespeare.
Their findings about differences in complexity of the Hamlet Quartos may be of interest for researchers concerned with questions of dependencies and absolute dating of those early prints. As to the complexity values for several translations of Othello it was astonishing to see that NoFear Shakespeare had a very high complexity measure compared to adaptations which did not try to facilitate the reading.
Toolbox for Dramamining – comparison of Othello-Translations
This stems from the fact that NoFear Shakespeare is much more narrative in its structure and tries to tidy up difficult tropes . This leads to the fact that the words to follow are much less foreseeable than in other translations which results in a higher complexity value. It is clear that this is only one aspect of complexity which has to be combined with other ones to cover ideas of complexity usually refered to in literary criticism. With this in mind it will be most interesting to see how these and other of their findings on plot unfolding and network analysis relate to traditional Shakespeare criticism.
Manuel Burghardt and Thomas Wilhelm (University of Regensburg) presented their Tool for Computing and Visualizing aspects of dramatic texts (Interaktive Analyse und Visualisierung von Dramen). They introduced their project of analyzing Plays of the Folger Shakespeare Library which allows to quickly overview who is present on stage in which scene and who is talking to whom in what length.
Together we are now heading for extending the tool as to the aim to analyze not only Shakespeares plays but also German plays from TextGrid and computing some more values like speech length, speech number and configuration density to see how they develop over the course of dramatic history of the 17th and 18th century.
In my talk Configuration density as a measurement for differences between comedies and tragedies I tested a measure to tell comedies and tragedies apart which had been computed for the first time by Solomon Marcus. Configuration density is a term for the average population density, i. e. the number of characters on stage in relation to the overall number of characters. As comedies are likely to have more characters on stage in average than tragedies where characters often miss each other tragically I assumed that comedies would have a much higher configuration density than tragedies. As a first step to inquire wether configuration density is a differentia specifica of tragedies and comedies in German plays of the 17th and 18th century I computed the density of dramatic texts from Andreas Gryphius, Christian Weise and Gotthold Ephraim Lessing available in TextGrid. Although there is a trend that the genres differ in the average configuration density there are some outliers. For my small corpus they can be explained by the structure of the plays: Play in play, episodic structure, choires lower the overall configuration density of comedies, for example. During the next months I will try to prove these findings for a set of 200 plays.
Peggy Bockwinkel und Marcus Willand (University of Stuttgart) questioned the assumption that the title-character also is the protagonist of a drama. (Die dramatische Figur im Dramentitel und ihre Funktion für die dramatische Handlung. Eine konsistente Relation?) Therefore they compared the number of speeches and tokens of a character (activity) with the number of references to this very character by others (presence). Thus, they computed an activity marker which allows to contrast activity patterns of prototypical roles (like king, queen, servant et cetera). Another question is wether the title-character is also the central node of the network of a drama. Having the already determined activity of a character in mind, Bockwinkel/Willand pushed the idea of building networks with edges equivalent to the frequency of the dialogue between dramatic agents. By way of example they showed that Emilia Galotti in Lessing’s drama with the same title is not very active compared to the number of speeches of other characters. Rather Marinelli, the intriguer, is the central character of the play regarding to activity and centrality, not Emilia – even though the drama bears her name. However, her indirect presence is very high, which means that she is talked about frequently and is therefore more likely the trigger of what the scholars call the core dramatic issue than the one in charge to solve this issue. On a corpus, preliminary containing mostly bürgerliche Trauerspiele (originating from TextGrid) they are now aiming to show possible patterns in the centrality of the title-character regarding literary periods and genres in further detail.
Peer Trilcke, Frank Fischer (University of Göttingen) and Dario Kampkaspar (Herzog-August-Bibliothek Wolfenbüttel) inquired wether the computation of scenic copresenc of characters represents changes in literary history and allows to specifiy the differences between works of the literary mainstream and exceptional ones (Digitale Netzwerkanalyse dramatischer Texte). For this purpose they calculated network information for 150 plays from TextGrid of the time between 1730 and 1799. They showed that there is a gain of the average number of dramatis personae at about 1770 – exactly at the beginning of the Storm and Stress period. At the same time we can witness a rise of the average degree, the number of direct relationships of a character to other characters. Comedies, tragicomedies and tragedies differ significantly as to thedensity of their networks and the number of dramatis personae. Considering the widley approved exceptionality of the plays of Goethe and Schiller they showed that these plays differ clearly from the mainstream with regard to their number of dramatis personae and their degree. See the slides here.
Jonathan Reeve (Modern Language Association) extracted Dialog from Shakespeare’s kings and compared it statistically with dialog from a number of other character groups, such as Shakespeare’s queens, servants, and fools, in an effort to computationally identify characteristic trends in the language of each group (»Imperial Voices«: Gender and Social Class among Shakespeare’s Characters, a Stylometric Approach). Distinctive word patterns are then identified in each group of characters. He concluded that relationships between these groups may be divined through this analysis, even suggesting implicit Shakespearean class hierarchies and gender relations. As I marvel at the method of this paper I would very much appreciate to test it with material from German drama. Read full paper.
Christof Schoech (University of Würzburg) focused on thematic aspects of genre with the help of topic modelling (Topic Modeling Literary Genre). In analyzing a collection of French Drama of the Classical Age and the Enlightenment he aimed at discovering whether different dramatic genres have distinctive dominant topics and inversely, whether clustering methods based on either most relevant topic words or overall topic scores produce groupings of texts which agree with more traditional genre distinctions.
The three most distinctive topics for comedies, with their top-ranked words, are tp49 (“aimer-parler”), tp56 (“monsieur-homme”) and tp32 (“beau-croire”). The three most distinctive topics for tragedy are tp36 (“coeur-amour-croire”), tp41 (“soin-jour”) and tp00 (“dieu-sang”), followed very closely by tp21 (“coeur-amour-cruel”). This shows that in tragedy, love is no less of an essential issue than in comedy, but in a very different perspective and associated, especially in tp21, with cruelty and misery.
In several cases, interesting patterns of topic importance over text progression can be discovered. This is the case, for example, for tp30 (“hymen-époux-coeur-foi”), a topic related to socially and divinely consecrated marriage and associated with both comedy and tragedy. In tragedy, tp30 is of a much larger concern initially but then continually drops in importance, with marriage becoming either increasingly difficult or less of a concern. In comedy, on the contrary, tp30 is of somewhat lower but constant relevance during the first four fifths of the play, but then increases drastically in the last fifth of the play.
Topic-based principal component analysis shows that the topics structure the text collection along the lines of genre. The first component (horizontal axis) clearly divides the plays into comedies (in the two quadrants to the left) and the tragedies (in the two quadrants to the right). The second component seems to be related to the difference between the two main genres and tragi-comedy, which can only be found in the upper two quadrants of the plot. However, there is substantial overlap between tragicomedy and, especially, tragedy. Based on topics, tragicomedy seems closer to tragedy than to comedy.
The paper has just been accepted for the Göttingen Dialog in Digital Humanities
The workshop showed that there are diverging questions, needs and manners of solutions according to subjects involved. In short it seems to me that
- linguists think of useful possibilities to enrich their linguistically deep tagged corpora with semantic annotations which allow to pose questions of literary critics.
- Scholars from computer science either work in collaboration with literary critics and linguists or try to exploit concepts themselves e.g. from information theory for the analysis of dramatic texts.
- Literary critics either aim to prove assumptions already known or hitherto unquestioned in literary criticism with quantitative data. Or they try to find unknown structures as well as semantic aspects in large corpora accessible with the help of computers for the first time. For them it is most relevant to explalin too which known features of a text aspects of language or computable features of the structure (for example of networks or configurations) correspond or provide deeper insights in. This means that even if one is providing a completely new values it has to be linked to aspects of texts or meaningful structures already known – be it as a synonym, be it as a new aspect.
It seems to me that projects in digitally assisted analysis of drama should always have at least one specialist from each subject (not to forget a person who really knows about statistics).