Big Five Personality Recognition from Multiple Text Genres

Santos, Vitor Garcia dos; Paraboni, Ivandré; Silva, Barbara Barbosa Claudino

doi:10.1007/978-3-319-64206-2_4

Cited by 13 publications

(7 citation statements)

References 11 publications

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“…, 2012). Several studies have been conducted on predicting the personality from written texts on social networks (dos Santos et al. , 2017; Ramezani et al.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Performance prediction of basketball players using automated personality mining with twitter data

Siemon

Wessels

2022

SBM

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PurposeThe purpose of this paper is to use Twitter data to mine personality traits of basketball players to predict their performance in the National Basketball Association (NBA).Design/methodology/approachAutomated personality mining and robotic process automation were used to gather data (player statistics and big five personality traits) of n = 185 professional basketball players. Correlation analysis and multiple linear regressions were computed to predict the performance of their NBA careers based on previous college performance and personality traits.FindingsAutomated personality mining of Tweets can be used to gather additional information about basketball players. Extraversion, agreeableness and conscientiousness correlate with basketball performance and can be used, in combination with previous game statistics, to predict future performance.Originality/valueThe study presents a novel approach to use automated personality mining of Twitter data as a predictor for future basketball performance. The contribution advances the understanding of the importance of personality for sports performance and the use of cognitive systems (automated personality mining) and the social media data for predictions. Scouts can use our findings to enhance their recruiting criteria in a multi-million dollar business, such as the NBA.

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“…, 2012). Several studies have been conducted on predicting the personality from written texts on social networks (dos Santos et al. , 2017; Ramezani et al.…”

Section: Theoretical Backgroundmentioning

confidence: 99%

Performance prediction of basketball players using automated personality mining with twitter data

Siemon

Wessels

2022

SBM

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“…Neste trabalho será utilizado o córpus b5 (Ramos et al, 2018) de textos em português brasileiro rotulados com escores de personalidade do modelo CGF relativos aos seus autores. O córpus foi utilizado em estudos prévios de geração de texto baseada em personalidade (Paraboni et al, 2017) e inferência de traços de personalidade a partir de textos e caracterização autoral (dos Santos et al, 2017;Hsieh et al, 2018;Silva & Paraboni, 2018b,a). Detalhes desta organização são discutidos por Ramos et al (2018).…”

Section: O Córpus B5unclassified

Reescrita sentencial baseada em traços de personalidade

Neto¹,

Paraboni

2020

Linguamática

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Sistemas de Geração de Língua Natural (GLN) são centrais para o desenvolvimento de comunicação humano-computador realista e psicologicamente plausível que não recorra ao uso de texto fixo ou predefinido, fazendo uso de uma ampla gama de estratégias para modelar alguma forma de variação estilística. Entre estas estratégias, o uso de modelos computacionais da personalidade humana emergiu como uma alternativa popular na área. Neste contexto, o presente trabalho apresenta um modelo de GLN do tipo texto-para-texto (ou reescrita sentencial) para o português que leva em conta, além da sentença a ser reescrita, informações sobre a personalidade de um locutor-alvo de interesse. Mais especificamente, o modelo transforma a sentença de entrada em outra na qual certas formas lexicais são substituídas por termos mais adequados ao tipo de personalidade-alvo fornecido. Resultados sugerem que as sentenças geradas com base em personalidade são mais próximas das que seriam produzidas por um locutor humano com as características de personalidade fornecidas do que seria possível sem acesso a essa informação, e abrem assim caminho para futuros estudos de geração de língua natural personalizada em português.

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“…Then, they concatenated the vectors with statistical features (like rate of emoticons, rate of capital letters and words, and total number of text posts of each user), to construct the input feature space for traditional regression algorithm to carry out final prediction in Big Five model. Santos et al [68] examined that which of the Big Five personality traits are best predicted by different text genres and the needed amount of text for doing the task appropriately. Dandannavar, et al [69] have surveyed personality prediction using social media text.…”

Section: Literature Reviewmentioning

confidence: 99%

Automatic Personality Prediction; an Enhanced Method Using Ensemble Modeling

Ramezani,

Feizi-Derakhshi,

Balafar

et al. 2020

Preprint

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Human personality is significantly represented by those words which he/she uses in his/her speech or writing. As a consequence of spreading the information infrastructures (specifically the Internet and social media), human communications have reformed notably from face to face communication. Generally, Automatic Personality Prediction (or Perception) (APP) is the automated forecasting of the personality on different types of human generated/exchanged contents (like text, speech, image, video, etc.). The major objective of this study is to enhance the accuracy of APP from the text. To this end, we suggest five new APP methods including term frequency vector-based, ontology-based, enriched ontology-based, latent semantic analysis (LSA)-based, and deep learning-based (BiLSTM) methods. These methods as the base ones, contribute to each other to enhance the APP accuracy through ensemble modeling (stacking) based on a hierarchical attention network (HAN) as the meta-model. The results show that ensemble modeling enhances the accuracy of APP.

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Big Five Personality Recognition from Multiple Text Genres

Cited by 13 publications

References 11 publications

Performance prediction of basketball players using automated personality mining with twitter data

Performance prediction of basketball players using automated personality mining with twitter data

Reescrita sentencial baseada em traços de personalidade

Automatic Personality Prediction; an Enhanced Method Using Ensemble Modeling

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