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  1. The rise and fall of biodiversity in literature: A comprehensive quantification of historical changes in the use of vernacular labels for biological taxa in Western creative literature

    Nature's non-material contributions to people are difficult to quantify and one aspect in particular, nature's contributions to communication (NCC), has so far been neglected. Recent advances in automated language processing tools enable us to... more

     

    Nature's non-material contributions to people are difficult to quantify and one aspect in particular, nature's contributions to communication (NCC), has so far been neglected. Recent advances in automated language processing tools enable us to quantify diversity patterns underlying the distribution of plant and animal taxon labels in creative literature, which we term BiL (biodiversity in literature). We assume BiL to provide a proxy for people's openness to nature's non-material contributions enhancing our understanding of NCC. We assembled a comprehensive list of 240,000 English biological taxon labels. We pre-processed and searched a subcorpus of digitised literature on Project Gutenberg for these labels. We quantified changes in biodiversity indices commonly used in ecological studies for 16,000 books, encompassing 4,000 authors, as proxies for BiL between 1705 and 1969. We observed hump-shape patterns for taxon label richness, abundance and Shannon diversity indicating a peak of BiL in the middle of the 19th century. This is also true for the ratio of biological to general lexical richness. The variation in label use between different sections within books, quantified as β-diversity, declined until the 1830s and recovered little, indicating a less specialised use of taxon labels over time. This pattern corroborates our hypothesis that before the onset of industrialisation BiL may have increased, reflecting several concomitant influences such as the general broadening of literary content, improved education and possibly an intensified awareness of the starting loss of biodiversity during the period of romanticism. Given that these positive trends continued and that we do not find support for alternative processes reducing BiL, such as language streamlining, we suggest that this pronounced trend reversal and subsequent decline of BiL over more than 100 years may be the consequence of humans’ increasing alienation from nature owing to major societal changes in the wake of industrialisation. We conclude that ...

     

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    Source: BASE Selection for Comparative Literature
    Language: English
    Media type: Article (journal)
    Format: Online
    DDC Categories: 800
    Subjects: biodiversity in literature; computational literary studies; cultural ecosystem services; environmental humanities; historical biodiversity; Nature's Contributions to People; non-material contribution; text mining
    Rights:

    info:eu-repo/semantics/openAccess

  2. Toward Multimodal Sentiment Analysis of Historic Plays: A Case Study with Text and Audio for Lessing’s Emilia Galotti
    Published: 2019
    Publisher:  CEUR-WS.org

    We present a case study as part of a work-in-progress project about multimodal sentiment analysis on historic German plays, taking Emilia Galotti by G. E. Lessing as our initial use case. We analyze the textual version and an audio version... more

     

    We present a case study as part of a work-in-progress project about multimodal sentiment analysis on historic German plays, taking Emilia Galotti by G. E. Lessing as our initial use case. We analyze the textual version and an audio version (audiobook). We focus on ready-to-use sentiment analysis methods: For the textual component, we implement a naive lexicon-based approach and another approach that enhances the lexicon by means of several NLP methods. For the audio analysis, we use the free version of the Vokaturi tool. We compare the results of all approaches and evaluate them against the annotations of a human expert, which serves as a gold standard. For our use case, we can show that audio and text sentiment analysis behave very differently: textual sentiment analysis tends to predict sentiment as rather negative and audio sentiment as rather positive. Compared to the gold standard, the textual sentiment analysis achieves accuracies of 56% while the accuracy for audio sentiment analysis is only 32%. We discuss possible reasons for these mediocre results and give an outlook on further steps we want to pursue in the context of multimodal sentiment analysis on historic plays.

     

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    Source: BASE Selection for Comparative Literature
    Language: English
    Media type: Conference object
    Format: Online
    DDC Categories: 800
    Subjects: computational literary studies; text mining; audio; audiobooks; drama; emotion analysis; Lessing; multimedia; multimodal; sentiment analysis
    Rights:

    info:eu-repo/semantics/openAccess

  3. Digital Environmental Humanities

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    Source: BASE Selection for Comparative Literature
    Language: German
    Media type: Conference object
    Format: Online
    DDC Categories: 800
    Subjects: environmental humanities; computational literary studies; text mining; Ökologie; Biodiversität; Inhaltsanalyse; Literatur
    Rights:

    info:eu-repo/semantics/openAccess

  4. Peeking Inside the DH Toolbox - Detection and Classification of Software Tools in DH Publications
    Published: 2022
    Publisher:  CEUR-WS.org

    Digital tools have played an important role in Digital Humanities (DH) since its beginnings. Accordingly, a lot of research has been dedicated to the documentation of tools as well as to the analysis of their impact from an epistemological... more

     

    Digital tools have played an important role in Digital Humanities (DH) since its beginnings. Accordingly, a lot of research has been dedicated to the documentation of tools as well as to the analysis of their impact from an epistemological perspective. In this paper we propose a binary and a multi-class classification approach to detect and classify tools. The approach builds on state-of-the-art neural language models. We test our model on two different corpora and report the results for different parameter configurations in two consecutive experiments. In the end, we demonstrate how the models can be used for actual tool detection and tool classification tasks in a large corpus of DH journals.

     

    Export to reference management software
    Source: BASE Selection for Comparative Literature
    Language: English
    Media type: Conference object
    Format: Online
    DDC Categories: 800
    Subjects: environmental humanities; computational literary studies; text mining; Ökologie; Biodiversität; Inhaltsanalyse; Literatur
    Rights:

    info:eu-repo/semantics/openAccess