Gender-preferential text mining of e-mail discourse

Corney, Malcolm; Vel, Olivier De; Anderson, Alison; Mohay, George M.

doi:10.1109/csac.2002.1176299

Cited by 91 publications

(64 citation statements)

References 12 publications

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“…Among the better known of these are Yule's K-measure (1944), Sichel's S-measure (1975), and Honore's Rmeasure (1979). Ultimately, none of these measures has proved especially useful on its own Grieve 2007), though it may be that these features have marginal value as additional inputs together with the features that we consider below (De Vel et al 2001;Corney et al 2002;Abbasi & Chen 2005;Zheng et al 2006;Li et al 2006;Abbasi & Chen 2008).…”

Section: Complexity Measuresmentioning

confidence: 99%

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Computational methods in authorship attribution

Koppel

Schler

Argamon

2008

J. Am. Soc. Inf. Sci.

442

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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Section: Complexity Measuresmentioning

confidence: 99%

“…Finally, for documents such as email, blogs and other online content, formatting and other structural features can also be profitably exploited for authorship attribution (De Vel et al 2001;Corney et al 2002;Abbasi & Chen 2008).…”

Section: Other Specialized Featuresmentioning

confidence: 99%

Computational methods in authorship attribution

Koppel

Schler

Argamon

2008

J. Am. Soc. Inf. Sci.

442

278

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“…Researchers have also attempted to automatically predict the gender of email senders using supervised learning techniques based on linguistic features (Corney et al, 2002;Cheng et al, 2011;Deitrick et al, 2012), a task we do not address in this paper. These studies use datasets that are relatively smaller in size.…”

Section: Related Workmentioning

confidence: 99%

“…These studies use datasets that are relatively smaller in size. Corney et al (2002) use around 4K emails from 325 gender identified authors. Cheng et al (2011) use around 9K emails from 108 gender identified authors.…”

Section: Related Workmentioning

confidence: 99%

Gender and Power: How Gender and Gender Environment Affect Manifestations of Power

Prabhakaran¹,

Reid²,

Rambow³

2014

Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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We investigate the interaction of power, gender, and language use in the Enron email corpus. We present a freely available extension to the Enron corpus, with the gender of senders of 87% messages reliably identified. Using this data, we test two specific hypotheses drawn from the sociolinguistic literature pertaining to gender and power: women managers use face-saving communicative strategies, and women use language more explicitly than men to create and maintain social relations. We introduce the notion of "gender environment" to the computational study of written conversations; we interpret this notion as the gender makeup of an email thread, and show that some manifestations of power differ significantly between gender environments. Finally, we show the utility of gender information in the problem of automatically predicting the direction of power between pairs of participants in email interactions.

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“…Starting from the surfacial properties, we measure the sentence, utterance and word length, including the proportion of words shorter than 4 or longer than 6 letters, frequency of each punctuation mark, 5 https://wordnet.princeton.edu/man/ lexnames.5WN.html 6 For complete overview refer to www.liwc.net and endings of each adjective as per Corney et al (2002). On the syntax level we measure the frequency of each part of speech as well as the 500 most frequent part-of-speech bi-, tri-and quadrigrams, and the frequency of each dependency obtained from the Stanford Parser.…”

Section: Classification Approach For Direct Speechmentioning

confidence: 99%

Personality Profiling of Fictional Characters using Sense-Level Links between Lexical Resources

Flek

Gurevych

2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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This study focuses on personality prediction of protagonists in novels based on the Five-Factor Model of personality. We present and publish a novel collaboratively built dataset of fictional character personality and design our task as a text classification problem. We incorporate a range of semantic features, including WordNet and VerbNet sense-level information and word vector representations. We evaluate three machine learning models based on the speech, actions and predicatives of the main characters, and show that especially the lexical-semantic features significantly outperform the baselines. The most predictive features correspond to reported findings in personality psychology.

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Gender-preferential text mining of e-mail discourse

Abstract: Abstract

Cited by 91 publications

References 12 publications

Computational methods in authorship attribution

Computational methods in authorship attribution

Gender and Power: How Gender and Gender Environment Affect Manifestations of Power

Personality Profiling of Fictional Characters using Sense-Level Links between Lexical Resources

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