Testing the Thematic Concentration of Text

Čech, Radek; Garabík, Radovan; Altmann, Gabriel

doi:10.1080/09296174.2015.1037157

Cited by 17 publications

(4 citation statements)

References 4 publications

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“…A central component of communication style is the tendency to stay on subject versus a penchant for bouncing between different semantic topics (Čech, Garabík, & Altmann, ; Wang & Liu, ). We use the LDA topic proportions to capture this tendency by calculating a Shannon entropy measure for each CEO.…”

Section: A New Approach To Measuring Communication Stylesmentioning

confidence: 99%

Machine learning approaches to facial and text analysis: Discovering CEO oral communication styles

Choudhury¹,

Wang²,

Carlson³

et al. 2019

Strategic Management Journal

117

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Research Summary We demonstrate how a novel synthesis of three methods—(a) unsupervised topic modeling of text data to generate new measures of textual variance, (b) sentiment analysis of text data, and (c) supervised ML coding of facial images with a cutting‐edge convolutional neural network algorithm—can shed light on questions related to CEO oral communication. With videos and corresponding transcripts of interviews with emerging market CEOs, we use this synthesis of methods to discover five distinct communication styles that incorporate both verbal and nonverbal aspects of communication. Our data comprises interviews that represent unedited expressions and content, making them especially suitable as data sources for the measurement of an individual's communication style. We then perform a proof‐of‐concept analysis, correlating CEO communication styles to M&A outcomes, highlighting the value of combining text and videographic data to define styles. We also discuss the benefits of using our methods versus current research methods. Managerial Summary CEOs spend most of their time communicating to investors, customers, and partners with the aim of influencing these various stakeholders. To what extent though does their effectiveness as leaders depend on a mixture of what they say and how they say it? We use cutting‐edge machine learning approaches to measure a CEO's communication style, which can give clues about the major strategic decisions a CEO's firm must make. With a collection of video interviews with 61 organizational leaders from emerging markets, we use textual analysis and facial image expression recognition to code whether CEOs are “excitable,” “stern,” “dramatic,” “rambling,” and “melancholy” in their communication styles. As a proof‐of‐concept, we also show that CEOs who were more dramatic in expressing themselves were also less likely to oversee major acquisitions. Therefore, not only can CEO communication styles help predict a firm's ability to grow, adapt to change, and reallocate existing assets, styles can also be coded more intuitively by using our new method, representing a vast improvement over previous methods in both accessibility and interpretability.

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Section: A New Approach To Measuring Communication Stylesmentioning

confidence: 99%

Machine learning approaches to facial and text analysis: Discovering CEO oral communication styles

Choudhury¹,

Wang²,

Carlson³

et al. 2019

Strategic Management Journal

117

View full text Add to dashboard Cite

show abstract

“…that aim at measuring syntactic complexity of written language have been applied in the study of political speeches (Degani, 2016; Schumacher and Eskenazi, 2016; Viser, 2015). As one of the important measures related to content analysis in quantitative linguistics, thematic concentration can indicate the writer’s or speaker’s intention to communicate certain themes more intensively than others (Čech et al, 2015). This method has been used in investigating the relationship between language and ideology (Čech, 2014), presidential inaugural speeches (Kubát and Čech, 2016a) and political debates (Savoy, 2017a).…”

Section: Introductionmentioning

confidence: 99%

“… 6. Čech (2014) demonstrates that the study of the word forms is almost irrelevant in this case, thus he and Čech et al (2015) computed the h-point value based on the frequency distribution of lemmas (i.e. canonical forms of words).…”

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confidence: 99%

Is Trump always rambling like a fourth-grade student? An analysis of stylistic features of Donald Trump’s political discourse during the 2016 election

Wang

Liu

2017

Discourse & Society

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During the 2016 election, Donald Trump was characterized by his simple, pompous and repetitive language. However, studies have shown that sometimes he does not speak the way he used to. Critical discourse analysis suggests that political discourse analysis should include both the characterization of the text and the systematic description of the context. Thus, this study intends to evaluate stylistic features of Donald Trump's political discourse based on the evolution over time as well as the genre variation by employing a comparison between Obama, Clinton and Trump. Results show that in debates, Trump uses less diverse vocabulary and simpler sentences. In campaign speeches, nonetheless, along with the change of circumstances, he sometimes employed a richer vocabulary and well-edited sentences. In addition, Trump's speeches contain relatively more central themes in his campaign speeches. His concentration on political themes may meet key interests of a large proportion of electorates.

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“…the terminological criterion to assess the knowledge centricity of legislative acts is the keywords from the thematic area of the text of the framework legislative act on rDS [14][15][16][17], whereas the criterion of science-centric orientation of any act is the presence in the text of the word "science" together with the first-order predicates. As evidenced by the results of the selection of slang thematic words for the definition of sciencecentered legislative acts using the scientometric (slang) method, the word "science", together with the first-order predicates, also belongs to the keywords of the text of the framework law [13][14][15].…”

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confidence: 99%

Scientometric Evaluation of Science-Centric Orientation of the National Legislation

Boublyk¹,

Bulkin²,

Mekh³

2020

Sci. innov.

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Introduction.The ability to build a scienceintensive economy in Ukraine depends on the implementation of strategic approaches to the formation and implementation of a unified government science and technology policy. One of the main conditions for the implementation of effective government policy is to amend, in terms of qual ity and quantity, the applicable laws and regulations with documents of scholarly research focus.Problem Statement. АActual task is to assess the sciencecentric orientation of legislative acts as a quantita tive criterion of "knowledge intensity" of the national legislation.Purpose. To obtain scientometric evaluation of sciencecentric orientation of the national legislative acts. Materials and Methods.The main methods of research are as follows: the scientometric (slang) method for the analysis of texts of legal acts and the statistical methods for structuring and comparative analysis of legisla tive acts as statistical units.Results. A survey of the statistical base of legislative acts of the supreme government bodies in 1992-2018 has revealed inconsistency in the managerial decisions of the top government authorities. In addition, there have been established periodic, stable statistical relationships between the intensity of scientific lawmaking and the expenditure on scholarly research and R&D activities. The original scientometric approaches to assessing the sciencecentric orientation of the national legislation have been developed.Conclusions. Within the period of independence, the main problems of public administration regarding sci ence and technology sphere in Ukraine have been as follows: competition for executive powers between the Cabi net of Ministry and the Presidential Administration; the focus of the Cabinet of Ministers on the redistribution of expenditures on science; the absence of strategic (longterm) approaches to the formation and implementation of a single R&D policy at the government level. It is necessary to reform the system of legislation of Ukraine for the sake of its sciencecentric orientation, in particular, because of the urgent need to harmonize it with the legislation of the European Union in the field of science, technology, and innovation.

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Testing the Thematic Concentration of Text

Cited by 17 publications

References 4 publications

Machine learning approaches to facial and text analysis: Discovering CEO oral communication styles

Machine learning approaches to facial and text analysis: Discovering CEO oral communication styles

Is Trump always rambling like a fourth-grade student? An analysis of stylistic features of Donald Trump’s political discourse during the 2016 election

Scientometric Evaluation of Science-Centric Orientation of the National Legislation

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