Towards Classification of Personality Prediction Model: A Combination of BERT Word Embedding and MLSMOTE

Lucky, Henry; Roslynlia,; Suhartono, Derwin

doi:10.1109/iccsai53272.2021.9609750

Cited by 5 publications

(11 citation statements)

References 19 publications

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“…On the contrary, context-specific methods are closer to a human in understanding language that consider the context of the words and inter-word correlations in a sentence to better understand the answers. Similar results are also reported on personality prediction using social media data, like tweets and Facebook posts, where context-specific methods (Christian et al, 2021;Lucky and Suhartono, 2021) achieved better performance than context-free methods, such as TF-IDF (Pratama and Sarno, 2015), LDA (Ong et al, 2017), and GloVe (Tandera et al, 2017).…”

Section: Discussionsupporting

confidence: 74%

“…Advances in machine learning and natural language processing (NLP) have catalyzed the growing body of evidence showing the relationship between one's language use and personality (Boyd and Pennebaker, 2017). This relationship has been demonstrated in both informal contexts such as social media (Gill et al, 2009;Golbeck et al, 2011;Iacobelli et al, 2011;Park et al, 2015;Christian et al, 2021;Lucky and Suhartono, 2021) as well as in formal contexts such as selfnarratives (Fast and Funder, 2008;Hirsh and Peterson, 2009), and job interviews (Jayaratne and Jayatilleke, 2020).…”

Section: Language and Personalitymentioning

confidence: 99%

“…The language-personality relationship has been utilized to develop predictive machine learning models to accurately infer personality traits from blogs (Iacobelli et al, 2011), essays (Neuman and Cohen, 2014), microblogs (Twitter, Sina Weibo) (Golbeck et al, 2011;Sumner et al, 2012;Xue et al, 2017;Lucky and Suhartono, 2021), social media posts (Tadesse et al, 2018;Wang et al, 2019), etc. The success of such attempts has led researchers to propose computer generated personality predictions to "complement-and in some instances replace-traditional self-report measures, which suffer from well-known response biases and are difficult to scale" (Hall and Matz, 2020).…”

Section: Language and Personalitymentioning

confidence: 99%

“…Open-vocabulary approaches are the current de facto standard for modeling language data and usually require a large amount of training data to learn the relationship between personality and language representation. Such predictive models have been demonstrated on textual data from social media with success (Schwartz et al, 2013;Park et al, 2015;Liu et al, 2016;Christian et al, 2021;Lucky and Suhartono, 2021).…”

Section: Language and Personalitymentioning

confidence: 99%

“…We introduce the details of using InterviewBERT for personality prediction in Section 3.2.3. Many NLP tasks achieve state-of-the-art performance with BERT based methods, including text-based personality predictions using social media text (Christian et al, 2021;Lucky and Suhartono, 2021). Our work remains novel in the context of predicting personality from interview responses using an extended version of the BERT model trained on a very large corpus of interview responses.…”

Section: Word and Document Representationsmentioning

confidence: 99%

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Explainable Personality Prediction Using Answers to Open-Ended Interview Questions

2022

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In this work, we demonstrate how textual content from answers to interview questions related to past behavior and situational judgement can be used to infer personality traits. We analyzed responses from over 58,000 job applicants who completed an online text-based interview that also included a personality questionnaire based on the HEXACO personality model to self-rate their personality. The inference model training utilizes a fine-tuned version of InterviewBERT, a pre-trained Bidirectional Encoder Representations from Transformers (BERT) model extended with a large interview answer corpus of over 3 million answers (over 330 million words). InterviewBERT is able to better contextualize interview responses based on the interview specific knowledge learnt from the answer corpus in addition to the general language knowledge already encoded in the initial pre-trained BERT. Further, the “Attention-based” learning approaches in InterviewBERT enable the development of explainable personality inference models that can address concerns of model explainability, a frequently raised issue when using machine learning models. We obtained an average correlation of r = 0.37 (p < 0.001) across the six HEXACO dimensions between the self-rated and the language-inferred trait scores with the highest correlation of r = 0.45 for Openness and the lowest of r = 0.28 for Agreeableness. We also show that the mean differences in inferred trait scores between male and female groups are similar to that reported by others using standard self-rated item inventories. Our results show the potential of using InterviewBERT to infer personality in an explainable manner using only the textual content of interview responses, making personality assessments more accessible and removing the subjective biases involved in human interviewer judgement of candidate personality.

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