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Sentiment Annotation of Historic German Plays: An Empirical Study on Annotation BehaviorSchmidt, Thomas, Burghardt, Manuel, Dennerlein, Katrin 29 May 2024 (has links)
We present results of a sentiment annotation study in the context of historical German plays. Our annotation corpus consists of 200
representative speeches from the German playwright Gotthold Ephraim Lessing. Six annotators, five non-experts and one expert in the
domain, annotated the speeches according to different sentiment annotation schemes. They had to annotate the differentiated polarity
(very negative, negative, neutral, mixed, positive, very positive), the binary polarity (positive/negative) and the occurrence of eight basic
emotions. After the annotation, the participants completed a questionnaire about their experience of the annotation process; additional
feedback was gathered in a closing interview. Analysis of the annotations shows that the agreement among annotators ranges from low
to mediocre. The non-expert annotators perceive the task as very challenging and report different problems in understanding the language
and the context. Although fewer problems occur for the expert annotator, we cannot find any differences in the agreement levels among
non-experts and between the expert and the non-experts. At the end of the paper, we discuss the implications of this study and future
research plans for this area
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Toward a Tool for Sentiment Analysis for German Historic PlaysSchmidt, Thomas, Burghardt, Manuel 05 June 2024 (has links)
No description available.
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Sentiment Annotation for Lessing’s Plays: Towards a Language Resource for Sentiment Analysis on German Literary TextsSchmidt, Thomas, Burghardt, Manuel, Dennerlein, Katrin, Wolff, Christian 05 June 2024 (has links)
We present first results of an ongoing research project on sentiment annotation of historical plays
by German playwright G. E. Lessing (1729-1781). For a subset of speeches from six of his most
famous plays, we gathered sentiment annotations by two independent annotators for each play. The
annotators were nine students from a Master’s program of German Literature. Overall, we gathered
annotations for 1,183 speeches. We report sentiment distributions and agreement metrics and put
the results in the context of current research. A preliminary version of the annotated corpus of
speeches is publicly available online and can be used for further investigations, evaluations and
computational sentiment analysis approaches.
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A Computational Approach to Analyzing Musical Complexity of the BeatlesBurghardt, Manuel, Fuchs, Florian 05 June 2024 (has links)
No description available.
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Katharsis – A Tool for Computational DrametricsSchmidt, Thomas, Burghardt, Manuel, Dennerlein, Katrin, Wolff, Christian 05 June 2024 (has links)
No description available.
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Digital Humanities in der Musikwissenschaft – Computergestützte Erschließungsstrategien und Analyseansätze für handschriftliche LiedblätterBurghardt, Manuel 23 May 2024 (has links)
Der Beitrag beschreibt ein laufendes Projekt zur computergestützten Erschließung und Analyse einer großen Sammlung handschriftlicher Liedblätter mit Volksliedern aus dem deutschsprachigen Raum. Am Beispiel dieses praktischen Projekts werden Chancen und Herausforderungen diskutiert, die der Einsatz von Digital Humanities-Methoden für den Bereich der Musikwissenschaft mit sich bringt.
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SubRosa – Multi-Feature-Ähnlichkeitsvergleiche von UntertitelnLuhmann, Jan, Burghardt, Manuel, Tiepmar, Jochen 20 June 2024 (has links)
No description available.
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„The Vectorian“ – Eine parametrisierbare Suchmaschine für intertextuelle ReferenzenLiebl, Bernhard, Burghardt, Manuel 20 June 2024 (has links)
No description available.
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Toward a Musical Sentiment (MuSe) Dataset for Affective Distant HearingAkiki, Christopher, Burghardt, Manuel 20 June 2024 (has links)
In this short paper we present work in progress that tries to leverage crowdsourced music metadata
and crowdsourced affective word norms to create a comprehensive dataset of music emotions, which
can be used for sentiment analyses in the music domain. We combine a mixture of different data
sources to create a new dataset of 90,408 songs with their associated embeddings in Russell’s model
of affect, with the dimensions valence, dominance and arousal. In addition, we provide a Spotify ID
for the songs, which can be used to add more metadata to the dataset via the Spotify API.
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From Historical Newspapers to Machine-Readable Data: The Origami OCR PipelineLiebl, Bernhard, Burghardt, Manuel 20 June 2024 (has links)
While historical newspapers recently have gained a lot of attention in the digital humanities, transforming them into machine-readable data by means of OCR poses some major challenges. In order
to address these challenges, we have developed an end-to-end OCR pipeline named Origami. This
pipeline is part of a current project on the digitization and quantitative analysis of the German
newspaper “Berliner Börsen-Zeitung” (BBZ), from 1872 to 1931. The Origami pipeline reuses existing open source OCR components and on top offers a new configurable architecture for layout
detection, a simple table recognition, a two-stage X-Y cut for reading order detection, and a new
robust implementation for document dewarping. In this paper we describe the different stages of the
workflow and discuss how they meet the above-mentioned challenges posed by historical newspapers.
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