Temporal predictive regression models for linguistic style analysis

Klaussner, Carmen; Vogel, Carl

doi:10.15398/jlm.v6i1.177

Cited by 7 publications

(5 citation statements)

References 17 publications

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“…On the other hand, earlier Shakespeare plays also showed a different styles, such as the rhyming iambic pentameter used by Shakespeare in plays written in 1594-1595 [Harbage, 1962]. Stylistic and linguistic analysis such as the use of certain rare words, rhymes, and the use of colloquialism-in-verse were also used to determine the exact date of creation of Shakespeare's plays [Taylor, 1987;Whitworth, 2003], indicating that Shakespeare's style has changed over time [Klaussner and Vogel, 2015;Klaussner, 2018;Klaussner and Vogel, 2018;Andreev, 2019;Seminck et al, 2021Seminck et al, , 2022. While these elements are not necessarily related to sentiments and other elements tested in this study, they are in agreement with the contention that the plays of Shakespeare exhibited differences in their style in different years.…”

Section: Resultsmentioning

confidence: 99%

A data science and machine learning approach to continuous analysis of Shakespeare's plays

Swisher

Shamir

2023

Journal of Data Mining &Amp; Digital Humanities

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The availability of quantitative text analysis methods has provided new ways of analyzing literature in a manner that was not available in the pre-information era. Here we apply comprehensive machine learning analysis to the work of William Shakespeare. The analysis shows clear changes in the style of writing over time, with the most significant changes in the sentence length, frequency of adjectives and adverbs, and the sentiments expressed in the text. Applying machine learning to make a stylometric prediction of the year of the play shows a Pearson correlation of 0.71 between the actual and predicted year, indicating that Shakespeare's writing style as reflected by the quantitative measurements changed over time. Additionally, it shows that the stylometrics of some of the plays is more similar to plays written either before or after the year they were written. For instance, Romeo and Juliet is dated 1596, but is more similar in stylometrics to plays written by Shakespeare after 1600. The source code for the analysis is available for free download.

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Section: Resultsmentioning

confidence: 99%

A data science and machine learning approach to continuous analysis of Shakespeare's plays

Swisher

Shamir

2023

Journal of Data Mining &Amp; Digital Humanities

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“…Commonly, the values of n that have been experimented with are {1,2,3,4,5}. Values higher than 3 could cause the data to become rather sparse [ 30 , 43 – 45 ]. As the value of n increased, the number of features also increased.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…A comparison of the general use of language (diachronic) in an author’s style was accomplished in [ 30 ]. Diachronic studies have considered the temporal ordering of an author’s works, seeking to reveal temporal changes within his or her style rather than the changes between authors or between different texts by the same author.…”

Section: Related Workmentioning

confidence: 99%

Detection of changes in literary writing style using N-grams as style markers and supervised machine learning

et al. 2022

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The analysis of an author’s writing style implies the characterization and identification of the style in terms of a set of features commonly called linguistic features. The analysis can be extrinsic, where the style of an author can be compared with other authors, or intrinsic, where the style of an author is identified through different stages of his life. Intrinsic analysis has been used, for example, to detect mental illness and the effects of aging. A key element of the analysis is the style markers used to model the author’s writing patterns. The style markers should handle diachronic changes and be thematic independent. One of the most commonly used style marker in extrinsic style analysis is n-gram. In this paper, we present the evaluation of traditional n-grams (words and characters) and dependency tree syntactic n-grams to solve the task of detecting changes in writing style over time. Our corpus consisted of novels by eleven English-speaking authors. The novels of each author were organized chronologically from the oldest to the most recent work according to the date of publication. Subsequently, two stages were defined: initial and final. In each stage three novels were assigned, novels of the initial stage corresponded to the oldest and those at the final stage to the most recent novels. To analyze changes in the writing style, novels were characterized by using four types of n-grams: characters, words, Part-Of-Speech (POS) tags and syntactic relations n-grams. Experiments were performed with a Logistic Regression classifier. Dimension reduction techniques such as Principal Component Analysis (PCA) and Latent Semantic Analysis (LSA) algorithms were evaluated. The results obtained with the different n-grams indicated that all authors presented significant changes in writing style over time. In addition, representations using n-grams of syntactic relations have achieved competitive results among different authors.

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“…We know that predictability is one of the critical concerns in various fields, including finance, climatology, tourism, sociology, and so on (see, e.g., [ 25 , 26 , 27 ]). However, as [ 10 ] shows, a small intercept can change the predictability dramatically when the predicting variable is nearly integrated.…”

Section: A Real Data Applicationmentioning

confidence: 99%

Testing the Intercept of a Balanced Predictive Regression Model

Wang

Liu

Fan

et al. 2022

Entropy

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Testing predictability is known to be an important issue for the balanced predictive regression model. Some unified testing statistics of desirable properties have been proposed, though their validity depends on a predefined assumption regarding whether or not an intercept term nevertheless exists. In fact, most financial data have endogenous or heteroscedasticity structure, and the existing intercept term test does not perform well in these cases. In this paper, we consider the testing for the intercept of the balanced predictive regression model. An empirical likelihood based testing statistic is developed, and its limit distribution is also derived under some mild conditions. We also provide some simulations and a real application to illustrate its merits in terms of both size and power properties.

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Temporal predictive regression models for linguistic style analysis

Cited by 7 publications

References 17 publications

A data science and machine learning approach to continuous analysis of Shakespeare's plays

A data science and machine learning approach to continuous analysis of Shakespeare's plays

Detection of changes in literary writing style using N-grams as style markers and supervised machine learning

Testing the Intercept of a Balanced Predictive Regression Model

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