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  1. Computational Sentiment Analysis of an 18th Century Corpus of Moravian English Memoirs
    Author: McGuire, Michael
    Published: 2021
    Publisher:  ProQuest Dissertations & Theses, Ann Arbor

    This dissertation uses digital humanities methods and computational linguistic tools to perform a sentiment analysis on a corpus of 18th century Moravian English personal memoirs. This historical corpus contains important similarities to modern... more

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    Location:
    Max-Planck-Institut für Bildungsforschung, Bibliothek und wissenschaftliche Information
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    This dissertation uses digital humanities methods and computational linguistic tools to perform a sentiment analysis on a corpus of 18th century Moravian English personal memoirs. This historical corpus contains important similarities to modern natural language text, requiring similar computational tools and approaches. The methodology of this sentiment analysis, considering the human social and cultural context in the design and implementation process of computational tools has compelling relevance to modern society and computing. The Moravian "Lebenslauf" or memoir represents a unique autobiographical genre, providing rare insight into social and personal lives of individuals living in an 18th century historical community. Sentiment analysis was performed using a custom lexicon and rule-based approach, adapting existing technology to a challenging and previously untested domain. Several different methods of sentiment scoring, analysis, and visualization were used, including narrative trendlines, word frequency, topic identification, and comparison of changing sentiment across time. Results reflect historical Moravian society, some community specific and others timeless and universally relatable as human feelings, and concepts. Split positive and negative sentiment is associated with illness and death, reflecting a positive perception as Moravians compared personal suffering to suffering of Christ during the passion while death meant salvation and "going home". Increasing life expectancy and decreasing young deaths from infectious disease, correlated with increasing negative sentiment, and decreasing positive sentiment associated with illness and death. Analysis by gender reflected unique Moravian gender constructs in 18th century Moravian society, often affecting sentiment. Moravian "blood and wounds" theology, peaking from the 1730s-1750s was detected using topic-specific sentiment vocabulary. Methodology relied on consideration of human social and cultural context and awareness of possible bias and limitations in computational tools during design, implantation, and analysis. These considerations in design allowed for creation of better domain-adapted tools. This methodology is directly relevant and consequential to modern computing problems involving interaction with human context, including serious concerns about ethics, privacy, and bias associated with computational tools and algorithms. Similar methodological approaches considering human context in computational design, implantation, and use can greatly improve future computational technology as it becomes increasingly indispensable to modern society.

     
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    Source: Union catalogues
    Language: English
    Media type: Dissertation
    Format: Online
    ISBN: 9798460499250
    Series: Dissertations Abstracts International
    Subjects: Linguistics; Information technology; Information science; Computational linguistics; Digital humanities; Moravian memoirs; Natural language processing; Sentiment analysis
    Scope: 1 Online-Ressource (1 electronic resource (286 pages))
    Notes:

    Source: Dissertations Abstracts International, Volume: 83-05, Section: B. - Advisors: Kubler, Sandra Committee members: Fowler, George; Riddell, Allen; Vance, Barbara; Faull, Katherine

    Ph.D., Indiana University, 2021.
  2. Autistic Characters
    (De)Coding Embedded Sentiment
    Author: Lopez, Jessica Colleen Perez
    Published: 2020
    Publisher:  ProQuest Dissertations & Theses, Ann Arbor

    Through the convergence of disability studies and literary cognitive studies, Autistic Characters: (de)coding embedded sentiment explores depictions of autistic characters in literature with the use of close readings and scaled readings, a... more

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    Max-Planck-Institut für Bildungsforschung, Bibliothek und wissenschaftliche Information
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    Through the convergence of disability studies and literary cognitive studies, Autistic Characters: (de)coding embedded sentiment explores depictions of autistic characters in literature with the use of close readings and scaled readings, a computational analytics method which uses sentiment analysis to decode the sentiment embedded in texts. I investigate these characters through close readings in which I explore my positionality within the major fields of study and the embedded medical and social histories coded into neuroatypical and neurodiverse literary representations of autism. Building upon the perspectives of my positionality and these histories, I explore how the substrate of literature is coded for a neurotypical and ableist focused reading. In my continued exploration of the embedded sentiment in literary constructions, I build upon the traditional close readings of autistic characters as I expand this analysis to conduct a (de)coding by scaled readings through which I produce visual representations from net sentiment (positive minus negative), total sentiment (positive plus absolute value of negative), negative sentiment, and positive sentiment measurements. These sets of visualizations are created both by chapters and in evenly spaced 500-word intervals throughout a full-length novel. To generate these scaled readings through the digital humanities method of sentiment analysis with the lexicon "bing," I use the programming language "R" to reveal the sentiment that lies latent within the texts. The visual patterns that emerge from the scaled readings provide graphical depictions from the positive and negative sentiment which allows me to re-read the text to analyze how it is coded with patterns, providing both a precise and different reading. I then further explore the origins of the code in the sentiment lexicon "bing" that generates the "positive" and "negative" data points. In this exploration, I critically examine the accuracy of this method and problematic constructions that arise from human generated lists that are used by machine learning to gauge the sentiment of words. Yet despite inaccuracies that may arise with scaled readings in combination with the biases of the lexicons, the visual patterns provide for a method of re-reading with sentiment that has not yet been explored. A method of reading that can lead to a different understanding of how the positive and negative embedded substrate generates charged sentiments which contribute to priming narrative feelings and in turn influences receptions of autistic characters.

     
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    Source: Union catalogues
    Language: English
    Media type: Dissertation
    Format: Online
    ISBN: 9798698593164
    Series: Dissertations Abstracts International
    Subjects: English literature; Disability studies; Reading instruction; Communication; Language arts; Neurosciences; Social research; Digital humanities; Literary cognitive studies; Neurodiversity; Scaled reading; Sentiment analysis; Autistic characters; Social histories; Focused reading; Machine learning; Lexicons
    Scope: 1 Online-Ressource (1 electronic resource (368 pages))
    Notes:

    Source: Dissertations Abstracts International, Volume: 82-06, Section: B. - Advisors: Phillips, Natalie M.; Fitzpatrick, Kathleen Committee members: Justus Nieland; Gary Hoppenstand; Zarena Aslami

    Ph.D., Michigan State University, 2020.
  3. Data analytics in digital humanities
    Contributor: Hai-Jew, Shalin (Publisher)
    Published: 2018; 2017
    Publisher:  Springer International Publishing, Cham ; Springer

    Location:
    Freie Universität Berlin, Universitätsbibliothek
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    Source: Philologische Bibliothek, FU Berlin
    Contributor: Hai-Jew, Shalin (Publisher)
    Language: English
    Media type: Book
    ISBN: 9783319854083; 3319854089
    Other identifier:
    9783319854083
    RVK Categories: AK 39950
    Edition: Softcover reprint of the original 1st edition 2017
    Series: Multimedia Systems and Applications
    Subjects: Datenanalyse; Digital Humanities
    Other subjects: UT; Cultural heritage semantics; Data analytics; Digital humanities; Inheritable digital codebooks; LIWC2015; Library science in DH; Literary corpora; Machine parody detection; Narratives; Related tags networks; Sentiment analysis; UKN; UYQ; UT
    Scope: xxii, 295 Seiten, Illustrationen
  4. 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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    Universitätsbibliothek Bamberg
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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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    Content information
    Volltext (URL des Erstveröffentlichers)
    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)
  5. 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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    TU Darmstadt, Universitäts- und Landesbibliothek - Stadtmitte
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    Universität Frankfurt, Elektronische Ressourcen
    Signature:
    /
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    Universitätsbibliothek Gießen
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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)
  6. Computational Sentiment Analysis of an 18th Century Corpus of Moravian English Memoirs
    Author: McGuire, Michael
    Published: 2021
    Publisher:  ProQuest Dissertations & Theses, Ann Arbor

    This dissertation uses digital humanities methods and computational linguistic tools to perform a sentiment analysis on a corpus of 18th century Moravian English personal memoirs. This historical corpus contains important similarities to modern... more

    Access:
    Aggregator (lizenzpflichtig)

    Location:
    Max-Planck-Institut für Bildungsforschung, Bibliothek und wissenschaftliche Information
    Inter-library loan:
    No inter-library loan

     

    This dissertation uses digital humanities methods and computational linguistic tools to perform a sentiment analysis on a corpus of 18th century Moravian English personal memoirs. This historical corpus contains important similarities to modern natural language text, requiring similar computational tools and approaches. The methodology of this sentiment analysis, considering the human social and cultural context in the design and implementation process of computational tools has compelling relevance to modern society and computing. The Moravian "Lebenslauf" or memoir represents a unique autobiographical genre, providing rare insight into social and personal lives of individuals living in an 18th century historical community. Sentiment analysis was performed using a custom lexicon and rule-based approach, adapting existing technology to a challenging and previously untested domain. Several different methods of sentiment scoring, analysis, and visualization were used, including narrative trendlines, word frequency, topic identification, and comparison of changing sentiment across time. Results reflect historical Moravian society, some community specific and others timeless and universally relatable as human feelings, and concepts. Split positive and negative sentiment is associated with illness and death, reflecting a positive perception as Moravians compared personal suffering to suffering of Christ during the passion while death meant salvation and "going home". Increasing life expectancy and decreasing young deaths from infectious disease, correlated with increasing negative sentiment, and decreasing positive sentiment associated with illness and death. Analysis by gender reflected unique Moravian gender constructs in 18th century Moravian society, often affecting sentiment. Moravian "blood and wounds" theology, peaking from the 1730s-1750s was detected using topic-specific sentiment vocabulary. Methodology relied on consideration of human social and cultural context and awareness of possible bias and limitations in computational tools during design, implantation, and analysis. These considerations in design allowed for creation of better domain-adapted tools. This methodology is directly relevant and consequential to modern computing problems involving interaction with human context, including serious concerns about ethics, privacy, and bias associated with computational tools and algorithms. Similar methodological approaches considering human context in computational design, implantation, and use can greatly improve future computational technology as it becomes increasingly indispensable to modern society.

     
    Export to reference management software   RIS file
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    Source: Union catalogues
    Language: English
    Media type: Dissertation
    Format: Online
    ISBN: 9798460499250
    Series: Dissertations Abstracts International
    Subjects: Linguistics; Information technology; Information science; Computational linguistics; Digital humanities; Moravian memoirs; Natural language processing; Sentiment analysis
    Scope: 1 Online-Ressource (1 electronic resource (286 pages))
    Notes:

    Source: Dissertations Abstracts International, Volume: 83-05, Section: B. - Advisors: Kubler, Sandra Committee members: Fowler, George; Riddell, Allen; Vance, Barbara; Faull, Katherine

    Ph.D., Indiana University, 2021.
  7. Autistic Characters
    (De)Coding Embedded Sentiment
    Author: Lopez, Jessica Colleen Perez
    Published: 2020
    Publisher:  ProQuest Dissertations & Theses, Ann Arbor

    Through the convergence of disability studies and literary cognitive studies, Autistic Characters: (de)coding embedded sentiment explores depictions of autistic characters in literature with the use of close readings and scaled readings, a... more

    Access:
    Aggregator (lizenzpflichtig)

    Location:
    Max-Planck-Institut für Bildungsforschung, Bibliothek und wissenschaftliche Information
    Inter-library loan:
    No inter-library loan

     

    Through the convergence of disability studies and literary cognitive studies, Autistic Characters: (de)coding embedded sentiment explores depictions of autistic characters in literature with the use of close readings and scaled readings, a computational analytics method which uses sentiment analysis to decode the sentiment embedded in texts. I investigate these characters through close readings in which I explore my positionality within the major fields of study and the embedded medical and social histories coded into neuroatypical and neurodiverse literary representations of autism. Building upon the perspectives of my positionality and these histories, I explore how the substrate of literature is coded for a neurotypical and ableist focused reading. In my continued exploration of the embedded sentiment in literary constructions, I build upon the traditional close readings of autistic characters as I expand this analysis to conduct a (de)coding by scaled readings through which I produce visual representations from net sentiment (positive minus negative), total sentiment (positive plus absolute value of negative), negative sentiment, and positive sentiment measurements. These sets of visualizations are created both by chapters and in evenly spaced 500-word intervals throughout a full-length novel. To generate these scaled readings through the digital humanities method of sentiment analysis with the lexicon "bing," I use the programming language "R" to reveal the sentiment that lies latent within the texts. The visual patterns that emerge from the scaled readings provide graphical depictions from the positive and negative sentiment which allows me to re-read the text to analyze how it is coded with patterns, providing both a precise and different reading. I then further explore the origins of the code in the sentiment lexicon "bing" that generates the "positive" and "negative" data points. In this exploration, I critically examine the accuracy of this method and problematic constructions that arise from human generated lists that are used by machine learning to gauge the sentiment of words. Yet despite inaccuracies that may arise with scaled readings in combination with the biases of the lexicons, the visual patterns provide for a method of re-reading with sentiment that has not yet been explored. A method of reading that can lead to a different understanding of how the positive and negative embedded substrate generates charged sentiments which contribute to priming narrative feelings and in turn influences receptions of autistic characters.

     
    Export to reference management software   RIS file
      BibTeX file
    Source: Union catalogues
    Language: English
    Media type: Dissertation
    Format: Online
    ISBN: 9798698593164
    Series: Dissertations Abstracts International
    Subjects: English literature; Disability studies; Reading instruction; Communication; Language arts; Neurosciences; Social research; Digital humanities; Literary cognitive studies; Neurodiversity; Scaled reading; Sentiment analysis; Autistic characters; Social histories; Focused reading; Machine learning; Lexicons
    Scope: 1 Online-Ressource (1 electronic resource (368 pages))
    Notes:

    Source: Dissertations Abstracts International, Volume: 82-06, Section: B. - Advisors: Phillips, Natalie M.; Fitzpatrick, Kathleen Committee members: Justus Nieland; Gary Hoppenstand; Zarena Aslami

    Ph.D., Michigan State University, 2020.
  8. Commons museums
    pedagogies for taking ownership of what is lost
    Author: Juliastuti, Nuraini
    Published: [2024]; ©2024
    Publisher:  ICI Berlin Press, Berlin

    This chapbook centres pedagogy within a new model of museum practice that prioritizes community. It focuses on two cultural institutions in Indonesia, the Pagesangan School in Yogyakarta and the Lakoat Kujawas in Mollo, East Nusa Tenggara, and uses... more

    Location:
    Institute for Cultural Inquiry- Kulturlabor, Bibliothek
    Signature:
    AM111 J85 2024
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    Unlimited inter-library loan, copies and loan

     

    This chapbook centres pedagogy within a new model of museum practice that prioritizes community. It focuses on two cultural institutions in Indonesia, the Pagesangan School in Yogyakarta and the Lakoat Kujawas in Mollo, East Nusa Tenggara, and uses the concept of the 'commons museums', which encompasses heritage, memory, and knowledge production to shape futures. The historical theft of cultural heritage and the extraction of natural resources are situated in Indonesia's post-Reformation context, with collective archives becoming methodologies for survival. The commons museum expands perspectives around restitution, foregrounding collective research and community struggles as instruments for restoring justice and recovering knowledge.

     
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Print
    ISBN: 9783965580701
    Series: Worlding Public Cultures ; 4
    Subjects: Education; Sentiment analysis; Collective memory; Community education; Decolonization; Commons museum; Community-based cultural spaces; Museum organization
    Scope: viii, 89 Seiten, 17 Illustrationen, 17.8 x 12.7 cm
  9. Commons museums
    pedagogies for taking ownership of what is lost
    Author: Juliastuti, Nuraini
    Published: [2024]; © 2024
    Publisher:  ICI Berlin Press, Berlin

    This chapbook centres pedagogy within a new model of museum practice that prioritizes community. It focuses on two cultural institutions in Indonesia, the Pagesangan School in Yogyakarta and the Lakoat Kujawas in Mollo, East Nusa Tenggara, and uses... more

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    Institute for Cultural Inquiry- Kulturlabor, Bibliothek
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    Zentrum für Kunst und Medien Karlsruhe / Staatliche Hochschule für Gestaltung, Bibliothek
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    This chapbook centres pedagogy within a new model of museum practice that prioritizes community. It focuses on two cultural institutions in Indonesia, the Pagesangan School in Yogyakarta and the Lakoat Kujawas in Mollo, East Nusa Tenggara, and uses the concept of the 'commons museums', which encompasses heritage, memory, and knowledge production to shape futures. The historical theft of cultural heritage and the extraction of natural resources are situated in Indonesia's post-Reformation context, with collective archives becoming methodologies for survival. The commons museum expands perspectives around restitution, foregrounding collective research and community struggles as instruments for restoring justice and recovering knowledge.

     
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    Source: Union catalogues
    Language: English
    Media type: Ebook
    Format: Online
    ISBN: 9783965580718
    Other identifier:
    Series: Worlding Public Cultures ; 4
    Subjects: Education; Sentiment analysis; Collective memory; Community education; Decolonization; Commons museum; Community-based cultural spaces; Museum organization
    Scope: 1 Online-Ressource (viii, 89 Seiten, 16,2 MB), Illustrationen
  10. Political competition
    how to measure party strategy in direct voter communication using social media data?
    Author: Sturm, Silke
    Published: 2019
    Publisher:  University of Hamburg, Chair of International Economics, Hamburg

    Political competition, party strategy and communication in the era of social media are growing issues. Due to the increasing social media presence of parties and voters alike, direct communication is more important for party... more

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    Staats- und Universitätsbibliothek Hamburg Carl von Ossietzky
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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    DS 614
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    Political competition, party strategy and communication in the era of social media are growing issues. Due to the increasing social media presence of parties and voters alike, direct communication is more important for party competition. This paper aims to improve the methodological approach used to analyze political competition and communication. The dataset includes over 30,000 Facebook status messages posted by seven German parties from January 2014 until February 2018. To pic modeling, which is commonly used in other fields, allows for evaluating party communication on a daily basis. The results show the high accuracy of calculating party - relevant issues. To determine the tone of the debate, a sentiment analysis was conduct ed. The prevalence of topics and sentiments over time allows for precise monitoring of the political debate.

     
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    Volltext (kostenfrei)
    Volltext (kostenfrei)
    Volltext (kostenfrei)
    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/191065
    Series: Hamburg discussion papers in international economics ; no. 1
    Subjects: Political competition; Party strategy; Decision making; Social media; Topic models; Sentiment analysis
    Scope: 1 Online-Ressource (30 Seiten), Illustrationen
  11. 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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    Staatsbibliothek zu Berlin - Preußischer Kulturbesitz, Haus Potsdamer Straße
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    Staats- und Universitätsbibliothek Bremen
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    Universitäts- und Landesbibliothek Sachsen-Anhalt / Zentrale
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    Gottfried Wilhelm Leibniz Bibliothek - Niedersächsische Landesbibliothek
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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)
  12. A sentiment-based risk indicator for the Mexican financial sector
    Author: Rho, Caterina; Fernández, Raúl; Palma, Brenda
    Published: May 2021
    Publisher:  Banco de México, [Ciudad de México, México]

    We apply text analysis to Twitter messages in Spanish to build a sentiment- based risk index for the financial sector in Mexico. We classify a sample of tweets for the period 2006-2019 to identify messages in response to positive or negative shocks... more

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    DS 192
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    We apply text analysis to Twitter messages in Spanish to build a sentiment- based risk index for the financial sector in Mexico. We classify a sample of tweets for the period 2006-2019 to identify messages in response to positive or negative shocks to the Mexican financial sector. We use a voting classifier to aggregate three different classifiers: one based on word polarities from a pre-defined dictionary; one based on a support vector machine; and one based on neural networks. Next, we compare our Twitter sentiment index with existing indicators of financial stress. We find that this novel index captures the impact of sources of financial stress not explicitly encompassed in quantitative risk measures. Finally, we show that a shock in our Twitter sentiment index correlates positively with an increase in financial market risk, stock market volatility, sovereign risk, and foreign exchange rate volatility.

     
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/240713
    Series: Working papers / Banco de México ; no 2021, 04
    Subjects: Sentiment analysis; systemic risk; banks
    Scope: 1 Online-Ressource (circa 66 Seiten), Illustrationen
  13. Bankruptcy prediction model based on business risk reports
    use of natural language processing techniques
    Author: Rasolomanana, Onjaniaina Mianin'Harizo
    Published: April.2021
    Publisher:  Faculty of Economics and Business, Hokkaido University, Sapporo, Japan

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    VS 817
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 2115/81088
    Series: Array ; no. 358 (2021)
    Subjects: Bankruptcy prediction; Business risk; Natural language processing; NLP; Sentiment analysis; Neural Networks
    Scope: 1 Online-Ressource (circa 16 Seiten)
  14. 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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    Location:
    Universitätsbibliothek Mannheim
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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)
  15. The impact of COVID-19 on analysts’ sentiment about the banking sector
    Author: Aguilar, Alicia; Torres, Diego
    Published: 2021
    Publisher:  Banco de España, Madrid

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    Location:
    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
    Signature:
    VS 470
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Series: Documentos de trabajo / Banco de España, Eurosistema ; no. 2124
    Subjects: Sentiment analysis; COVID-19 impact; European banking; analysts’ estimates
    Scope: 1 Online-Ressource (circa 47 Seiten), Illustrationen
  16. Big data and happiness
    Author: Rossouw, Stephanié; Greyling, Talita
    Published: 2020
    Publisher:  Global Labor Organization (GLO), Essen

    The pursuit of happiness. What does that mean? Perhaps a more prominent question to ask is, 'how does one know whether people have succeeded in their pursuit'? Survey data, thus far, has served us well in determining where people see themselves on... more

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    ZBW - Leibniz-Informationszentrum Wirtschaft, Standort Kiel
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    DS 565
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    The pursuit of happiness. What does that mean? Perhaps a more prominent question to ask is, 'how does one know whether people have succeeded in their pursuit'? Survey data, thus far, has served us well in determining where people see themselves on their journey. However, in an everchanging world, one needs high-frequency data instead of data released with significant time-lags. High-frequency data, which stems from Big Data, allows policymakers access to virtually real-time information that can assist in effective decision-making to increase the quality of life for all. Additionally, Big Data collected from, for example, social media platforms give researchers unprecedented insight into human behaviour, allowing significant future predictive powers.

     
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    Source: Union catalogues
    Language: English
    Media type: Book
    Format: Online
    Other identifier:
    hdl: 10419/223012
    Series: GLO discussion paper ; no. 634
    Subjects: Happiness; Big Data; Sentiment analysis
    Scope: 1 Online-Ressource (circa 38 Seiten), Illustrationen
  17. 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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    Helmut-Schmidt-Universität, Universität der Bundeswehr Hamburg, Universitätsbibliothek
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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
    Other identifier:
    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
  18. Data analytics in digital humanities /
    Contributor: Hai-Jew, Shalin (Publisher)
    Published: 2018.; 2017.
    Publisher:  Springer International Publishing,, Cham : ; Springer,

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    Freie Universität Berlin, Universitätsbibliothek, Zentralbibliothek
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    Source: Philologische Bibliothek, FU Berlin
    Contributor: Hai-Jew, Shalin (Publisher)
    Language: English
    Media type: Book
    Format: Print
    ISBN: 978-3-319-85408-3; 3-319-85408-9
    Other identifier:
    9783319854083
    RVK Categories: AK 39950
    Edition: Softcover reprint of the original 1st edition 2017
    Series: Multimedia Systems and Applications
    Subjects: Digital Humanities; Datenanalyse
    Other subjects: UT; Cultural heritage semantics; Data analytics; Digital humanities; Inheritable digital codebooks; LIWC2015; Library science in DH; Literary corpora; Machine parody detection; Narratives; Related tags networks; Sentiment analysis; UKN; UYQ
    Scope: xxii, 295 Seiten :, Illustrationen.
  19. Multimodal Approaches to Speaker State Identification: Emotion, Sentiment, and Novel Modalities
    Author: Bachman
    Published: 2024
    Publisher:  tredition, view line

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  20. Data Analytics in Digital Humanities
    Contributor: Hai-Jew, Shalin (Publisher)
    Published: 2018
    Publisher:  Springer International Publishing, Cham ; Springer

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  21. Data analytics in digital humanities
    Contributor: Hai-Jew, Shalin (Publisher)
    Published: [2017]
    Publisher:  Springer, Cham

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    Source: Union catalogues
    Contributor: Hai-Jew, Shalin (Publisher)
    Language: English
    Media type: Book
    Format: Print
    ISBN: 9783319544984; 9783319544991; 3319544985
    Other identifier:
    9783319544984
    DDC Categories: 004
    Series: Multimedia systems and applications
    Other subjects: Cultural heritage semantics; Data analytics; Digital humanities; Inheritable digital codebooks; LIWC2015; Library science in DH; Literary corpora; Machine parody detection; Narratives; Related tags networks; Sentiment analysis
    Scope: xxii, 295 Seiten, Illustrationen, 23.5 cm x 15.5 cm, 0 g
    Notes:

    Enthält Literaturangaben
  22. 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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    Staatsbibliothek zu Berlin - Preußischer Kulturbesitz, Haus Unter den Linden
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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)