Profile and Objectives
First project phase (2020-2023)
The transition of realism, the dominant art form of the 19th Century, to early modernism at the turn to the 20th Century has been seen by contemporaries then and literary history since then as a profound change affecting many formal and content-related aspects. This is also true for poetry. But while today only a small group of poets and poems is seen as ‘modern’, the contemporaries applied this attribute to much more texts as can be seen, for example, by looking at all the anthologies with modern poetry published around 1900. One of the main goals is to understand this discrepancy: Does literary history ignore the modern trends in those other poems or did the contemporaries perceive change and innovation where there was none? In order to answer this question we will look at the similarity of texts, assuming that more modern texts will be more similar with each other than with more traditional texts. Similarity is always related to a specific perspective. The dimensions under which contemporaries then and literary historians today see the main differences between the poetry of realism and early modernism are mostly the same: new themes, new forms and a new way of addressing and expressing emotions in poems. We will cover all these aspects but with different degrees of effort to innovate the methods used. The development of new methods will concentrate on semantic text similarity and sentiment or more exact emotion analysis. Measuring the text similarity of short texts like poems is quite challenging, but since the introduction of word2vec and other forms of word embeddings the situation has improved dramatically. Applying these approaches to historical texts and especially to a genre like poetry is another challenge: The vocabulary of poetry is markedly different even compared to that of other literary genres. It is characterized by the usage of old-fashioned words and neologisms, many of which are compound words. Determining which approach to word embeddings is preferable for our use cases and how they can be used to represent short texts focusing on dimensions like general semantics is one focus. The other is the development of an historical sentiment lexicon including emotions without anachronism.
Second project phase (2023-2026)
The goal of our project is to better understand how poetry developed in the context of literature and culture in the second half of the 19th Century. For this purpose we extend the investigations of the first project phase on the transition from realist to early modernist poetry by modeling this transition in a more complex way and we switch from a perspective that looks solely at poetry to a view of the genre in a wider context. We want to achieve this by (1) analyzing the development of poetry over time, (2) focusing on three parameters of change, (3) analyzing them along three textual dimensions, (4) including literary and extra-literary contexts, and (5) examining to what extent poetry is influenced by these contexts, influences them or is independent of them by integrating partial measurements into one model.
(1) Although conceiving of literary history as a linear succession of separate periods might be a helpful heuristic, literary change de facto proceeds gradually along a temporal continuum, accelerating or decelerating dynamically. Therefore, our goal is to not only contrast realism and early modernism as two separate periods, but also to analyze time series data on an annual basis that provide a more fine-grained view of literary change. Encoding texts and data with time signatures also enables a more informed analysis of the interrelation of different data sets, which is important for the investigation of contextual influences.
(2) As mentioned above, literary history has hypothesized that the change from realism to early modernism can be understood as a pluralization of literary positions. Not only does the number of possible positions increase, but many of them lie outside of what was formerly possible. Researchers discuss this transition with regard to a wide variety of phenomena; we focus on three particularly important parameters of change: Gender roles and gender relations, technology, philosophical and scientific anthropology (Darwin and Nietzsche). There are undoubtedly other parameters relevant to the transition of realism to early modernism and worthy of investigation, for example social issues or big cities. However, we believe that the analysis of these three parameters is fruitful, since researchers repeatedly consider them relevant to understand this transition, and they represent the full range of context types most often mentioned in literary studies, namely social-constructive contexts, social- and cultural-historical contexts, and contexts of the history of ideas.
(3) In order to operationalize the hypotheses from literary studies, we will analyze texts and contexts along three textual dimensions which we decided on for theoretical and analytical reasons: content, emotion, and style. For all dimensions, we use existing or develop new methods that allow us to analyze them in terms of distinctive features and to relate contextual data and literary texts from different genres to each other with regard to the parameters of change.
(4) As indicated, we include not only poems. In order to cover a wide variety of context sources, which literary histories have found important for the development of poetry, we add texts from other literary genres and non-literary texts that represent public discourses to our corpus. In addition, we include data on social and cultural aspects.
(5) In CLS projects, contextual relationships have not often been part of a formal modeling of literary change. Their inclusion, however, might allow us to not only describe literary change, but also to take first steps towards its explanation in a corpus- and data-based way. We aim for a time-series-forecasting model, which has the advantage that all variables can be predictor and target, depending on their temporal order. Ultimately, we aim to merge our different data into one integrative model.