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  1. The shapes of stories
    sentiment analysis for narrative
    Author: Elkins, Katherine
    Published: 2022
    Publisher:  Cambridge University Press, Cambridge

    Sentiment analysis has gained widespread adoption in many fields, but not-until now-in literary studies. Scholars have lacked a robust methodology that adapts the tool to the skills and questions central to literary scholars. Also lacking has been... more

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    Sentiment analysis has gained widespread adoption in many fields, but not-until now-in literary studies. Scholars have lacked a robust methodology that adapts the tool to the skills and questions central to literary scholars. Also lacking has been quantitative data to help the scholar choose between the many models. Which model is best for which narrative, and why? By comparing over three dozen models, including the latest Deep Learning AI, the author details how to choose the correct model-or set of models-depending on the unique affective fingerprint of a narrative. The author also demonstrates how to combine a clustered close reading of textual cruxes in order to interpret a narrative. By analyzing a diverse and cross-cultural range of texts in a series of case studies, the Element highlights new insights into the many shapes of stories

     
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    Source: Union catalogues
    Language: English
    Media type: Ebook
    Format: Online
    ISBN: 9781009270403
    Other identifier:
    Series: Cambridge elements
    Subjects: Criticism / Data processing; Sentiment analysis
    Scope: 1 Online-Ressource (115 Seiten)
    Notes:

    Title from publisher's bibliographic system (viewed on 25 Jul 2022)
  2. The shapes of stories
    sentiment analysis for narrative
    Author: Elkins, Katherine L.
    Published: 2022
    Publisher:  Cambridge University Press, Cambridge

    Sentiment analysis has gained widespread adoption in many fields, but not-until now-in literary studies. Scholars have lacked a robust methodology that adapts the tool to the skills and questions central to literary scholars. Also lacking has been... more

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    Sentiment analysis has gained widespread adoption in many fields, but not-until now-in literary studies. Scholars have lacked a robust methodology that adapts the tool to the skills and questions central to literary scholars. Also lacking has been quantitative data to help the scholar choose between the many models. Which model is best for which narrative, and why? By comparing over three dozen models, including the latest Deep Learning AI, the author details how to choose the correct model-or set of models-depending on the unique affective fingerprint of a narrative. The author also demonstrates how to combine a clustered close reading of textual cruxes in order to interpret a narrative. By analyzing a diverse and cross-cultural range of texts in a series of case studies, the Element highlights new insights into the many shapes of stories.

     
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    Source: Union catalogues
    Language: English
    Media type: Ebook
    Format: Online
    ISBN: 9781009270403
    Other identifier:
    Series: Cambridge elements. Elements in digital literary studies,
    Subjects: Criticism; Sentiment analysis
    Scope: 1 Online-Ressource (115 pages)
  3. The shapes of stories
    sentiment analysis for narrative
    Author: Elkins, Katherine
    Published: 2022
    Publisher:  Cambridge University Press, Cambridge

    Sentiment analysis has gained widespread adoption in many fields, but not - until now - in literary studies. Scholars have lacked a robust methodology that adapts the tool to the skills and questions central to literary scholars. Also lacking has... more

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    Fachinformationsverbund Internationale Beziehungen und Länderkunde
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    Sentiment analysis has gained widespread adoption in many fields, but not - until now - in literary studies. Scholars have lacked a robust methodology that adapts the tool to the skills and questions central to literary scholars. Also lacking has been quantitative data to help the scholar choose between the many models. Which model is best for which narrative, and why? By comparing over three dozen models, including the latest Deep Learning AI, the author details how to choose the correct model - or set of models - depending on the unique affective fingerprint of a narrative. The author also demonstrates how to combine a clustered close reading of textual cruxes in order to interpret a narrative. By analyzing a diverse and cross-cultural range of texts in a series of case studies, the Element highlights new insights into the many shapes of stories.

     
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    Source: Staatsbibliothek zu Berlin
    Language: English
    Media type: Ebook
    Format: Online
    ISBN: 9781009270403; 9781009270397
    Other identifier:
    Series: Cambridge elements. Elements in digital literary studies
    Subjects: Criticism; Sentiment analysis
    Scope: 1 online resource (115 pages), digital, PDF file(s).
    Notes:

    Title from publisher's bibliographic system (viewed on 25 Jul 2022)
  4. Is bitcoin a better safe-haven asset for individual investors than gold?
    evidence from sanctioned russia
    Author: Zhang, Lu; Wolff, Christiaan Cornelis Petrus
    Published: 12 December 2022
    Publisher:  Centre for Economic Policy Research, London

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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Series: Array ; DP17745
    Subjects: Hedging performance; Dynamic conditional correlation; Sentiment analysis
    Scope: 1 Online-Ressource (circa 23 Seiten)
  5. Understanding Sentiment Through Context
    Author: Crowley, Richard M.; Wong, M. H. Franco
    Published: 2022
    Publisher:  SSRN, [S.l.]

    We examine whether empirical results using text-based sentiment of U.S. annual reports depend on the underlying context, within documents, from which sentiment is measured. We construct a clause-level measure of context, showing that sentiment is... more

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    We examine whether empirical results using text-based sentiment of U.S. annual reports depend on the underlying context, within documents, from which sentiment is measured. We construct a clause-level measure of context, showing that sentiment is driven by many different contexts and that positive and negative sentiment are driven by different contexts. We then construct context-level sentiment measures and examine whether sentiment works as expected at the context-level across four prediction problems. Our results demonstrate that document-level sentiment exhibits significant noise in prediction and suggest that document-level aggregation of sentiment leads to missed empirical nuances. The contexts driving sentiment results vary substantially by outcome, suggesting lower empirical internal validity for document-level sentiment. Using three additional sentiment measures, we document the same inferences, concluding that document-level aggregation likely leads to lower internal validity. Sentiment is thus best applied at the level of specific contexts rather than across whole documents

     
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
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    Series: Rotman School of Management Working Paper ; No. 4316229
    Subjects: Sentiment analysis; context; machine learning; aggregation; lasso regression; text analysis
    Other subjects: Array
    Scope: 1 Online-Ressource (79 p)
    Notes:

    Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments December 30, 2022 erstellt
  6. The shapes of stories
    sentiment analysis for narrative
    Author: Elkins, Katherine
    Published: 2022
    Publisher:  Cambridge University Press, Cambridge

    Sentiment analysis has gained widespread adoption in many fields, but not - until now - in literary studies. Scholars have lacked a robust methodology that adapts the tool to the skills and questions central to literary scholars. Also lacking has... more

    Access:
    Resolving-System (lizenzpflichtig)

    Location:
    Staatsbibliothek zu Berlin - Preußischer Kulturbesitz, Haus Unter den Linden
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    Unlimited inter-library loan, copies and loan

     

    Sentiment analysis has gained widespread adoption in many fields, but not - until now - in literary studies. Scholars have lacked a robust methodology that adapts the tool to the skills and questions central to literary scholars. Also lacking has been quantitative data to help the scholar choose between the many models. Which model is best for which narrative, and why? By comparing over three dozen models, including the latest Deep Learning AI, the author details how to choose the correct model - or set of models - depending on the unique affective fingerprint of a narrative. The author also demonstrates how to combine a clustered close reading of textual cruxes in order to interpret a narrative. By analyzing a diverse and cross-cultural range of texts in a series of case studies, the Element highlights new insights into the many shapes of stories.

     
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    Source: Staatsbibliothek zu Berlin
    Language: English
    Media type: Ebook
    Format: Online
    ISBN: 9781009270403; 9781009270397
    Other identifier:
    Series: Cambridge elements. Elements in digital literary studies
    Subjects: Criticism; Sentiment analysis
    Scope: 1 online resource (115 pages), digital, PDF file(s).
    Notes:

    Title from publisher's bibliographic system (viewed on 25 Jul 2022)