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StyleExplorer: A Toolkit for Textual Writing Style Visualization

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Advances in Information Retrieval (ECIR 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11438))

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Abstract

The analysis of textual writing styles is a well-studied problem with ongoing and active research in fields like authorship attribution, author profiling, text segmentation or plagiarism detection. While many features have been proposed and shown to be effective to characterize authors or document types in terms of high-dimensional feature vectors, an intuitive, human-friendly view on the computed data is often lacking. For example, machine learning algorithms are able to attribute previously unseen documents to a set of known authors by utilizing those features, but a visualization of the most discriminating features is usually not provided. To this end, we present StyleExplorer, a freely available web tool that is able to extract textual features from documents and to visualize them in multiple variants. Besides analyzing single documents intrinsically, it is also possible to visually compare multiple documents in single views with respect to selected metrics, making it a valuable analysis tool for various tasks in natural language processing as well as for areas in the humanities that work and analyze textual data.

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Notes

  1. 1.

    Available at https://dbis-styleexplorer.uibk.ac.at, [Review login: ecir2019/ecir2019].

  2. 2.

    https://www.meteor.com, visited October 2018.

  3. 3.

    https://reactjs.org, visited October 2018.

  4. 4.

    And also be downloaded in JSON format for individual further postprocessing.

  5. 5.

    Utilizing Highcharts, https://www.highcharts.com, visited October 2018.

  6. 6.

    E.g., recent and popular techniques like word2vec [12].

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Correspondence to Michael Tschuggnall .

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Tschuggnall, M., Gerrier, T., Specht, G. (2019). StyleExplorer: A Toolkit for Textual Writing Style Visualization. In: Azzopardi, L., Stein, B., Fuhr, N., Mayr, P., Hauff, C., Hiemstra, D. (eds) Advances in Information Retrieval. ECIR 2019. Lecture Notes in Computer Science(), vol 11438. Springer, Cham. https://doi.org/10.1007/978-3-030-15719-7_28

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  • DOI: https://doi.org/10.1007/978-3-030-15719-7_28

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-15718-0

  • Online ISBN: 978-3-030-15719-7

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