Using Part-of-Speech Sequences Frequencies in a Text to Predict Author Personality: a Corpus Study

Litvinova, Tatiana; Seredin, P. V.; Litvinova, Olga

doi:10.17485/ijst/2015/v8is9/51103

Cited by 17 publications

(14 citation statements)

References 4 publications

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“…According to the literature, AP in social media has two main subtasks: age and gender detection (see [2,3,4,7,9,13,14,17,23,27,31,32,33,36,39,40,46] for gender, and [2,3,4,13,23,28,29,31,33,36,40] for age). Related tasks include personality prediction [22,21], interests identification [20,34] (a.k.a. genres), sentiment and emotion recognition [37], among others.…”

Section: Related Workmentioning

confidence: 99%

A visual approach for age and gender identification on Twitter

Álvarez‐Carmona

Pellegrin

Montes-y-Gómez

et al. 2018

IFS

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The goal of Author Profiling (AP) is to identify demographic aspects (e.g., age, gender) from a given set of authors by analyzing their written texts. Recently, the AP task has gained interest in many problems related to computer forensics, psychology, marketing, but specially in those related with social media exploitation. As known, social media data is shared through a wide range of modalities (e.g., text, images and audio), representing valuable information to be exploited for extracting valuable insights from users. Nevertheless, most of the current work in AP using social media data has been devoted to analyze textual information only, and there are very few works that have started exploring the gender identification using visual information. Contrastingly, this paper focuses in exploiting the visual modality to perform both age and gender identification in social media, specifically in Twitter. Our goal is to evaluate the pertinence of using visual information in solving the AP task. Accordingly, we have extended the Twitter corpus from PAN 2014, incorporating posted images from all the users, making a distinction between tweeted and retweeted images. Performed experiments provide interesting evidence on the usefulness of visual information in comparison with traditional textual representations for the AP task.

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Section: Related Workmentioning

confidence: 99%

A visual approach for age and gender identification on Twitter

Álvarez‐Carmona

Pellegrin

Montes-y-Gómez

et al. 2018

IFS

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“…Corpus. This study utilised a specially designed corpus designed for authorship profiling study RusPersonality as well as a constantly growing text corpus (Litvinova, 2014;Litvinova et al, 2015), both of which contained, aside from the texts themselves, metadata with information about the authors (gender, age, education, psychological testing data, etc.). The corpus currently contains more than 2000 texts obtained from more than 1 000 respondents, including descriptions of pictures and a letter to a friend.…”

Section: Empirical Study: Gender Attribution In Russian Languge Writtmentioning

confidence: 99%

“…This makes it impossible to design a functional model as part of a multiparameter regression. Therefore, it was decided to use not only a multiparameter regression model as we did in Litvinova (2014) and Litvinova et. al.…”

Section: Empirical Study: Gender Attribution In Russian Languge Writtmentioning

confidence: 99%

Gender identification in Russian written texts

Litvinova¹,

Seredin²,

Litvinova³

et al. 2017

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This article examines the identification of the gender of authors of Russian written texts using the quantitative parameters analysis approach. Identification of the gender of authors of texts is viewed as part of authorship profiling task. The material used for the study was a specially designed corpus of Russian texts "RusPersonality" which (along with other Slavic languages) has obtained little attention in authorship profiling studies. We made use of high-frequency text parameters occurring in texts of diffetent topics and genres. The correlation analysis data obtained using Russian texts were compared with those in other languages. The regression analysis was employed. The suggested approach allows one to identify gender as accurately as 64% using only 5 parameters.

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“…Lithuanian parliamentary texts were used to identify the speaker's age, gender and political view in Kapočiūtė-Dzikienė et al (2014). A study of Russian showed there is a correlation between POS-bigrams and a person's gender and personality (Litvinova et al, 2015). Another relevant contribution to the field for Russian was the interdisciplinary approach to identifying the risk of self-destructive behavior .…”

Section: Introductionmentioning

confidence: 99%

Gender Profiling for Slovene Twitter communication: the Influence of Gender Marking, Content and Style

Verhoeven¹,

Škrjanec²,

Pollak³

2017

Proceedings of the 6th Workshop on Balto-Slavic Natural Language Processing

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We present results of the first gender classification experiments on Slovene text to our knowledge. Inspired by the TwiSty corpus and experiments , we employed the Janes corpus and its gender annotations to perform gender classification experiments on Twitter text comparing a token-based and a lemma-based approach. We find that the token-based approach (92.6% accuracy), containing gender markings related to the author, outperforms the lemma-based approach by about 5%. Especially in the lemmatized version, we also observe stylistic and contentbased differences in writing between men (e.g., more profane language, numerals and beer mentions) and women (e.g., more pronouns, emoticons and character flooding). Many of our findings corroborate previous research on other languages.

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Using Part-of-Speech Sequences Frequencies in a Text to Predict Author Personality: a Corpus Study

Cited by 17 publications

References 4 publications

A visual approach for age and gender identification on Twitter

A visual approach for age and gender identification on Twitter

Gender identification in Russian written texts

Gender Profiling for Slovene Twitter communication: the Influence of Gender Marking, Content and Style

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