A Language-independent and Compositional Model for Personality Trait
            Recognition from Short Texts

Liu, Fei; Pérez, Julien; Nowson, Scott

doi:10.18653/v1/e17-1071

Cited by 37 publications

(25 citation statements)

References 24 publications

(45 reference statements)

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“…As regards the three studies that used RMSE, Liu et al [62] achieved an average of ∼.11 over the five traits, considerably smaller (i.e., better) than the average ∼.79 and ∼.28−.52, reported respectively by Quercia et al [56] and Carducci et al [48].…”

Section: Personality Detection From Textmentioning

confidence: 86%

A large-scale, in-depth analysis of developers’ personalities in the Apache ecosystem

Calefato

Lanubile²,

Vasilescu³

2019

Information and Software Technology

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Context: Large-scale distributed projects are typically the results of collective efforts performed by multiple developers with heterogeneous personalities.Objective: We aim to find evidence that personalities can explain developers' behavior in large scale-distributed projects. For example, the propensity to trust others -a critical factor for the success of global software engineering -has been found to influence positively the result of code reviews in distributed projects.Method: In this paper, we perform a quantitative analysis of ecosystem-level data from the code commits and email messages contributed by the developers working on the Apache Software Foundation (ASF) projects, as representative of large scale-distributed projects.Results: We find that there are three common types of personality profiles among Apache developers, characterized in particular by their level of Agreeableness and Neuroticism. We also confirm that developers' personality is stable over time. Moreover, personality traits do not vary with their role, membership, and extent of contribution to the projects. We also find evidence that more open developers are more likely to make contributors to Apache projects.Conclusion: Overall, our findings reinforce the need for future studies on human factors in software engineering to use psychometric tools to control for differences in developers' personalities.

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Section: Personality Detection From Textmentioning

confidence: 86%

A large-scale, in-depth analysis of developers’ personalities in the Apache ecosystem

Calefato

Lanubile²,

Vasilescu³

2019

Information and Software Technology

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“…Quercia et al [22] were the first who explored at large the relationship between personality and use of Twitter; they also proposed a model to infer users' personality based on just following, followers, and listed count numbers. Similarly, Jusupova et al [23] used demographic and social activity information to predict personality of Portuguese users, whereas Liu et al [24] proposed a deep-learning-based approach to build hierarchical word and sentence representations that is able to infer personality of users from three languages: English, Italian, and Spanish. Van de Ven et al [25] based their analyses on LinkedIn, a job-related social media platform, yet they did not find strong correlations between personality traits and user profiles, except for Extraversion.…”

Section: Related Workmentioning

confidence: 99%

TwitPersonality: Computing Personality Traits from Tweets Using Word Embeddings and Supervised Learning

et al. 2018

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Abstract:We are what we do, like, and say. Numerous research efforts have been pushed towards the automatic assessment of personality dimensions relying on a set of information gathered from social media platforms such as list of friends, interests of musics and movies, endorsements and likes an individual has ever performed. Turning this information into signals and giving them as inputs to supervised learning approaches has resulted in being particularly effective and accurate in computing personality traits and types. Despite the demonstrated accuracy of these approaches, the sheer amount of information needed to put in place such a methodology and access restrictions make them unfeasible to be used in a real usage scenario. In this paper, we propose a supervised learning approach to compute personality traits by only relying on what an individual tweets about publicly. The approach segments tweets in tokens, then it learns word vector representations as embeddings that are then used to feed a supervised learner classifier. We demonstrate the effectiveness of the approach by measuring the mean squared error of the learned model using an international benchmark of Facebook status updates. We also test the transfer learning predictive power of this model with an in-house built benchmark created by twenty four panelists who performed a state-of-the-art psychological survey and we observe a good conversion of the model while analyzing their Twitter posts towards the personality traits extracted from the survey.

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“…Iacobelli et al (2011) test different extraction settings with stop words and inverse document frequency for predicting personality in a large corpus of blogs using support vector machines (SVM) as a classifier. Liu et al (2017) use Twitter user posts and propose a deep-learning-based model utilizing a character-level bi-directional recurrent neural network. Arnoux et al (2017) build a personality prediction model for Twitter users that utilizes word embedding with Gaussian processes (Rasmussen and Williams, 2005).…”

Section: Related Workmentioning

confidence: 99%

Incorporating Textual Information on User Behavior for Personality Prediction

Yamada

Sasano

Takeda

2019

Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

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Several recent studies have shown that textual information of user posts and user behaviors such as liking and sharing the specific posts are useful for predicting the personality of social media users. However, less attention has been paid to the textual information derived from the user behaviors. In this paper, we investigate the effect of textual information on user behaviors for personality prediction. Our experiments on the personality prediction of Twitter users show that the textual information of user behaviors is more useful than the cooccurrence information of the user behaviors. They also show that taking user behaviors into account is crucial for predicting the personality of users who do not post frequently.

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A Language-independent and Compositional Model for Personality Trait Recognition from Short Texts

Cited by 37 publications

References 24 publications

A large-scale, in-depth analysis of developers’ personalities in the Apache ecosystem

A large-scale, in-depth analysis of developers’ personalities in the Apache ecosystem

TwitPersonality: Computing Personality Traits from Tweets Using Word Embeddings and Supervised Learning

Incorporating Textual Information on User Behavior for Personality Prediction

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