A Hybrid Deep Learning Technique for Personality Trait Classification From Text

Recently, Cognitive-based Sentiment Analysis with emphasis on automatic detection of user behaviour, such as personality traits, based on online social media text has gained a lot of attention. However, most of the existing works are based on conventional techniques, which are not sufficient to get...

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Autores principales:	Hussain Ahmad, Muhammad Usama Asghar, Muhammad Zubair Asghar, Aurangzeb Khan, Amir H. Mosavi
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Personality trait deep learning artificial intelligence convolutional neural network long short-term memory social networks Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/cb93996e8edc4532b875033eba453f24
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spelling	oai:doaj.org-article:cb93996e8edc4532b875033eba453f242021-11-09T00:01:36ZA Hybrid Deep Learning Technique for Personality Trait Classification From Text2169-353610.1109/ACCESS.2021.3121791https://doaj.org/article/cb93996e8edc4532b875033eba453f242021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9583245/https://doaj.org/toc/2169-3536Recently, Cognitive-based Sentiment Analysis with emphasis on automatic detection of user behaviour, such as personality traits, based on online social media text has gained a lot of attention. However, most of the existing works are based on conventional techniques, which are not sufficient to get promising results. In this research work, we propose a hybrid Deep Learning-based model, namely Convolutional Neural Network concatenated with Long Short-Term Memory, to show the effectiveness of the proposed model for 8 important personality traits (Introversion-Extroversion, Intuition-Sensing, Thinking-Feeling, Judging-Perceiving). We implemented our experimental evaluations on the benchmark dataset to accomplish the personality trait classification task. Evaluations of the datasets have shown better results, which demonstrates that the proposed model can effectively classify the user’s personality traits as compared to the state-of-the-art techniques. Finally, we evaluate the effectiveness of our approach through statistical analysis. With the knowledge obtained from this research, organizations are capable of making their decisions regarding the recruitment of personals in an efficient way. Moreover, they can implement the information obtained from this research as best practices for the selection, management, and optimization of their policies, services, and products.Hussain AhmadMuhammad Usama AsgharMuhammad Zubair AsgharAurangzeb KhanAmir H. MosaviIEEEarticlePersonality traitdeep learningartificial intelligenceconvolutional neural networklong short-term memorysocial networksElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 146214-146232 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Personality trait deep learning artificial intelligence convolutional neural network long short-term memory social networks Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Personality trait deep learning artificial intelligence convolutional neural network long short-term memory social networks Electrical engineering. Electronics. Nuclear engineering TK1-9971 Hussain Ahmad Muhammad Usama Asghar Muhammad Zubair Asghar Aurangzeb Khan Amir H. Mosavi A Hybrid Deep Learning Technique for Personality Trait Classification From Text
description	Recently, Cognitive-based Sentiment Analysis with emphasis on automatic detection of user behaviour, such as personality traits, based on online social media text has gained a lot of attention. However, most of the existing works are based on conventional techniques, which are not sufficient to get promising results. In this research work, we propose a hybrid Deep Learning-based model, namely Convolutional Neural Network concatenated with Long Short-Term Memory, to show the effectiveness of the proposed model for 8 important personality traits (Introversion-Extroversion, Intuition-Sensing, Thinking-Feeling, Judging-Perceiving). We implemented our experimental evaluations on the benchmark dataset to accomplish the personality trait classification task. Evaluations of the datasets have shown better results, which demonstrates that the proposed model can effectively classify the user’s personality traits as compared to the state-of-the-art techniques. Finally, we evaluate the effectiveness of our approach through statistical analysis. With the knowledge obtained from this research, organizations are capable of making their decisions regarding the recruitment of personals in an efficient way. Moreover, they can implement the information obtained from this research as best practices for the selection, management, and optimization of their policies, services, and products.
format	article
author	Hussain Ahmad Muhammad Usama Asghar Muhammad Zubair Asghar Aurangzeb Khan Amir H. Mosavi
author_facet	Hussain Ahmad Muhammad Usama Asghar Muhammad Zubair Asghar Aurangzeb Khan Amir H. Mosavi
author_sort	Hussain Ahmad
title	A Hybrid Deep Learning Technique for Personality Trait Classification From Text
title_short	A Hybrid Deep Learning Technique for Personality Trait Classification From Text
title_full	A Hybrid Deep Learning Technique for Personality Trait Classification From Text
title_fullStr	A Hybrid Deep Learning Technique for Personality Trait Classification From Text
title_full_unstemmed	A Hybrid Deep Learning Technique for Personality Trait Classification From Text
title_sort	hybrid deep learning technique for personality trait classification from text
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/cb93996e8edc4532b875033eba453f24
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A Hybrid Deep Learning Technique for Personality Trait Classification From Text

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