Shakespeare, Fletcher, and the Two Noble Kinsmen

Ledger, Gerard; Merriam, Thomas

doi:10.1093/llc/9.3.235

Cited by 82 publications

(29 citation statements)

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“…This is particularly helpful, as identification of linguistic style is more reliable for lengthy texts (Grimmer & Steward, 2013: 272). Such observation has also been made in the field of authorship attribution analysis, where longer textual samples are considered to have greater discriminatory power in terms of distinguishing authorship characteristics (Baillie, 1974;Ledger & Merriam, 1994).…”

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 88%

Market reaction to the positiveness of annual report narratives

Yekini

Wisniewski

Millo

2016

The British Accounting Review

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This paper focuses on narratives published by UK companies, defined here as the content of annual reports excluding financial statements and notes to accounts. We endeavour to gauge the tone of these narratives by recording the frequency of positive words appearing in the text. We show that the extent of positiveness is related to market reaction around the disclosure date. This conclusion is maintained even after controlling for the financial figures that are reported simultaneously and company-specific characteristics. Consequently, narratives should not be perceived as mere impression management tools, but also as conduits for disseminating price-sensitive information.JEL codes: M41; G12; G14

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Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 88%

Market reaction to the positiveness of annual report narratives

Yekini

Wisniewski

Millo

2016

The British Accounting Review

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“…The first approaches focused mainly on stylometrics (Ledger and Merriam (1994), Holmes and Forsyth (1995), Baayen et al (1996), and Aaronson (2001)). More recent approaches use content-based features, such as Akiva and Koppel (2012) and Layton et al (2011).…”

Section: Unsupervised Document Classificationmentioning

confidence: 99%

Structure-based Clustering of Novels

Ardanuy¹,

Sporleder²

2014

Proceedings of the 3rd Workshop on Computational Linguistics for Literature (CLFL)

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To date, document clustering by genres or authors has been performed mostly by means of stylometric and content features. With the premise that novels are societies in miniature, we build social networks from novels as a strategy to quantify their plot and structure. From each social network, we extract a vector of features which characterizes the novel. We perform clustering over the vectors obtained, and the resulting groups are contrasted in terms of author and genre.

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“…Thus, Kjell (1994aKjell ( ,1994b and Kjell et al (1995) used relative frequencies of character n-grams for attribution of the Federalist papers and others have used character n-grams for authorship attribution of texts in English (Ledger & Merriam 1994;Clement and Sharp 2003;Houvardas and Stamatatos 2006;Stamatatos 2008), Dutch (Hoorn et al 1999), Russian (Kukushkina 2001), Italian (Benedetto et al 2002) and Greek (Keselj et al 2003;Peng et al 2004). Grieve (2007) has found that character bigrams work surprisingly well for attribution of newspaper opinion columns.…”

Section: Character N-gramsmentioning

confidence: 99%

Computational methods in authorship attribution

Koppel

Schler

Argamon

2008

J. Am. Soc. Inf. Sci.

462

278

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Statistical authorship attribution has a long history, culminating in the use of modern machine learning classification methods. Nevertheless, most of this work suffers from the limitation of assuming a small closed set of candidate authors and essentially unlimited training text for each. Real-life authorship attribution problems, however, typically fall short of this ideal. Thus, following detailed discussion of previous work, three scenarios are considered here for which solutions to the basic attribution problem are inadequate. In the first variant, the profiling problem, there is no candidate set at all; in this case, the challenge is to provide as much demographic or psychological information as possible about the author. In the second variant, the needle-in-a-haystack problem, there are many thousands of candidates for each of whom we might have a very limited writing sample.In the third variant, the verification problem, there is no closed candidate set but there is one suspect; in this case, the challenge is to determine if the suspect is or is not the author. For each variant, it is shown how machine learning methods can be adapted to handle the special challenges of that variant.

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Shakespeare, Fletcher, and the Two Noble Kinsmen

Cited by 82 publications

References 0 publications

Market reaction to the positiveness of annual report narratives

Market reaction to the positiveness of annual report narratives

Structure-based Clustering of Novels

Computational methods in authorship attribution

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