Using transformers and Bi-LSTM with sentence embeddings for prediction of openness human personality trait
Understanding human personality traits is significant as it helps in decision making related to consumers’ behavior, career counselling, team building and top candidates’ selection for recruitment. Among various traits, openness is essential as it shows both diverse aspects of sensitive nature or in...
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PeerJ Inc.
2025-05-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-2781.pdf |
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| author | Anam Naz Hikmat Ullah Khan Tariq Alsahfi Mousa Alhajlah Bader Alshemaimri Ali Daud |
| author_facet | Anam Naz Hikmat Ullah Khan Tariq Alsahfi Mousa Alhajlah Bader Alshemaimri Ali Daud |
| author_sort | Anam Naz |
| collection | DOAJ |
| description | Understanding human personality traits is significant as it helps in decision making related to consumers’ behavior, career counselling, team building and top candidates’ selection for recruitment. Among various traits, openness is essential as it shows both diverse aspects of sensitive nature or intuitive nature. The individuals having a sensing nature tends to be more practical and prefer to focus on concrete information whereas the users having intuitive trait type is characterized by a focus on abstract ideas, creative thinking and future-oriented perspectives. In this research work, we aim to explore diverse natural language processing (NLP) based features and apply state of the art deep learning algorithms for openness trait prediction. Using standard Myers-Briggs Type Indicator (MBTI) dataset, we propose the use of the latest deep features of sentence embeddings which captures contextual semantics of the content to be used with deep learning models. For comparison, we explore textual features of Frequency-Inverse Document (TF-IDF) and parts of speech (POS) tagging with machine learning models and deep features of word2vec and global vectors for word representation (GloVe) with deep learning models. The comprehensive empirical analysis reveals that TF-IDF used with gradient boosting achieves high accuracy of 90% whereas, the deep feature of sentence embeddings when used and with deep model bidirectional long short-term memory (Bi-LSTM) achieves 90.5% accuracy. The best results have been achieved using the latest Transformer-based DistilBERT, which achieves the highest accuracy of 92% outperforming the existing studies in relevant literature. |
| format | Article |
| id | doaj-art-8d14b18308484f80b72aeda31e45d2b8 |
| institution | OA Journals |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-8d14b18308484f80b72aeda31e45d2b82025-08-20T01:53:11ZengPeerJ Inc.PeerJ Computer Science2376-59922025-05-0111e278110.7717/peerj-cs.2781Using transformers and Bi-LSTM with sentence embeddings for prediction of openness human personality traitAnam Naz0Hikmat Ullah Khan1Tariq Alsahfi2Mousa Alhajlah3Bader Alshemaimri4Ali Daud5Department of Information Technology, University of Sargodha, Sargodha, Punjab, PakistanDepartment of Information Technology, University of Sargodha, Sargodha, Punjab, PakistanDepartment of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi ArabiaApplied of Computer Science College, King Saud University, Riyadh, Saudi ArabiaSoftware Engineering Department, College of Computing and Information Sciences, King Saud University, Riyadh, Saudi ArabiaFaculty of Resilience, Rabdan Academy, Abu Dhabi, United Arab EmiratesUnderstanding human personality traits is significant as it helps in decision making related to consumers’ behavior, career counselling, team building and top candidates’ selection for recruitment. Among various traits, openness is essential as it shows both diverse aspects of sensitive nature or intuitive nature. The individuals having a sensing nature tends to be more practical and prefer to focus on concrete information whereas the users having intuitive trait type is characterized by a focus on abstract ideas, creative thinking and future-oriented perspectives. In this research work, we aim to explore diverse natural language processing (NLP) based features and apply state of the art deep learning algorithms for openness trait prediction. Using standard Myers-Briggs Type Indicator (MBTI) dataset, we propose the use of the latest deep features of sentence embeddings which captures contextual semantics of the content to be used with deep learning models. For comparison, we explore textual features of Frequency-Inverse Document (TF-IDF) and parts of speech (POS) tagging with machine learning models and deep features of word2vec and global vectors for word representation (GloVe) with deep learning models. The comprehensive empirical analysis reveals that TF-IDF used with gradient boosting achieves high accuracy of 90% whereas, the deep feature of sentence embeddings when used and with deep model bidirectional long short-term memory (Bi-LSTM) achieves 90.5% accuracy. The best results have been achieved using the latest Transformer-based DistilBERT, which achieves the highest accuracy of 92% outperforming the existing studies in relevant literature.https://peerj.com/articles/cs-2781.pdfCognitive scienceDeep learningMachine learningPsychologyArtificial intelligenceFeature engineering |
| spellingShingle | Anam Naz Hikmat Ullah Khan Tariq Alsahfi Mousa Alhajlah Bader Alshemaimri Ali Daud Using transformers and Bi-LSTM with sentence embeddings for prediction of openness human personality trait PeerJ Computer Science Cognitive science Deep learning Machine learning Psychology Artificial intelligence Feature engineering |
| title | Using transformers and Bi-LSTM with sentence embeddings for prediction of openness human personality trait |
| title_full | Using transformers and Bi-LSTM with sentence embeddings for prediction of openness human personality trait |
| title_fullStr | Using transformers and Bi-LSTM with sentence embeddings for prediction of openness human personality trait |
| title_full_unstemmed | Using transformers and Bi-LSTM with sentence embeddings for prediction of openness human personality trait |
| title_short | Using transformers and Bi-LSTM with sentence embeddings for prediction of openness human personality trait |
| title_sort | using transformers and bi lstm with sentence embeddings for prediction of openness human personality trait |
| topic | Cognitive science Deep learning Machine learning Psychology Artificial intelligence Feature engineering |
| url | https://peerj.com/articles/cs-2781.pdf |
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