GlobalTrait: Personality Alignment of Multilingual Word Embeddings

We propose a multilingual model to recognize Big Five Personality traits from text data in four different languages: English, Spanish, Dutch and Italian. Our analysis shows that words having a similar semantic meaning in different languages do not necessarily correspond to the same personality traits. Therefore, we propose a personality alignment method, GlobalTrait, which has a mapping for each trait from the source language to the target language (English), such that words that correlate positively to each trait are close together in the multilingual vector space. Using these aligned embeddings for training, we can transfer personality related training features from high-resource languages such as English to other low-resource languages, and get better multilingual results, when compared to using simple monolingual and unaligned multilingual embeddings. We achieve an average F-score increase (across all three languages except English) from 65 to 73.4 (+8.4), when comparing our monolingual model to multilingual using CNN with personality aligned embeddings. We also show relatively good performance in the regression tasks, and better classification results when evaluating our model on a separate Chinese dataset.

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Text Analysis of Applicants for Personality Classification Using Multinomial Naïve Bayes and Decision Tree

JURNAL INFOTEL ◽

10.20895/infotel.v12i3.505 ◽

2020 ◽

Vol 12 (3) ◽

Author(s):

Nanda Yonda Hutama ◽

Kemas Muslim Lhaksmana ◽

Isman Kurniawan

Keyword(s):

Decision Tree ◽

Personality Traits ◽

Big Five ◽

Naive Bayes ◽

Naïve Bayes ◽

Classification Model ◽

Big Five Personality ◽

Text Data ◽

Personality Classification ◽

Best Parameters

Employees' qualities affect companies' performances and with a large number of applicants, it's difficult to find suitable applicants. To help with it, companies carry out psychological tests to know applicants' personalities, since personality's considered to have a relationship with work performances. But psychological testing requires a lot of effort, cost, and human resources. Thus with a system that can classify personalities through text can help reduce the effort needed. Similar studies carried out with the big five personalities as the theoretical basis and used one of the personality traits, namely using the k-NN method with 65% accuracy. Based on these studies, accuracy can improve by finding the best parameters using all of the big five personalities. This research is conducted based on the big five personality traits and related traits, namely consciousness and agreeableness. The data used is text data that's been labelled, pre-processed and feature selected. The clean text data is used to create a classification model using multinomial Naive Bayes and decision trees. There are 6 models built based on 3 work cultures, decision tree with an accuracy of 33%, 66%, 80%, and multinomial naïve Bayes with an accuracy of 83%, 50%, 60%, which resulted as better performance.

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Intelligence and Personality

Journal of Individual Differences ◽

10.1027/1614-0001/a000311 ◽

2020 ◽

Vol 41 (3) ◽

pp. 124-132

Author(s):

Marc-André Bédard ◽

Yann Le Corff

Keyword(s):

Personality Traits ◽

Big Five ◽

Real Life ◽

Big Five Personality Traits ◽

Original Study ◽

Life Assessment ◽

Big Five Personality ◽

Verbal Intelligence ◽

Nonverbal Intelligence ◽

French Canadian

Abstract. This replication and extension of DeYoung, Quilty, Peterson, and Gray’s (2014) study aimed to assess the unique variance of each of the 10 aspects of the Big Five personality traits ( DeYoung, Quilty, & Peterson, 2007 ) associated with intelligence and its dimensions. Personality aspects and intelligence were assessed in a sample of French-Canadian adults from real-life assessment settings ( n = 213). Results showed that the Intellect aspect was independently associated with g, verbal, and nonverbal intelligence while its counterpart Openness was independently related to verbal intelligence only, thus replicating the results of the original study. Independent associations were also found between Withdrawal, Industriousness and Assertiveness aspects and verbal intelligence, as well as between Withdrawal and Politeness aspects and nonverbal intelligence. Possible explanations for these associations are discussed.

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Personality of Clown Doctors

Journal of Individual Differences ◽

10.1027/1614-0001/a000187 ◽

2016 ◽

Vol 37 (1) ◽

pp. 49-55 ◽

Cited By ~ 5

Author(s):

Alberto Dionigi

Keyword(s):

General Population ◽

Personality Traits ◽

Big Five ◽

Normal Population ◽

Big Five Personality Traits ◽

Published Data ◽

Big Five Personality

Abstract. In recent years, both professional and volunteer clowns have become familiar in health settings. The clown represents a peculiar humorist’s character, strictly associated with the performer’s own personality. In this study, the Big Five personality traits (BFI) of 155 Italian clown doctors (130 volunteers and 25 professionals) were compared to published data for the normal population. This study highlighted specific differences between clown doctors and the general population: Clown doctors showed higher agreeableness, conscientiousness, openness, and extraversion, as well as lower neuroticism compared to other people. Moreover, specific differences emerged comparing volunteers and professionals: Professional clowns showed significantly lower in agreeableness compared to their unpaid colleagues. The results are also discussed with reference to previous studies conducted on groups of humorists. Clowns’ personalities showed some peculiarities that can help to explain the facility for their performances in the health setting and that are different than those of other groups of humorists.

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