Personality classification from text using bidirectional long short-term memory model

Document Type

Article

Source of Publication

Multimedia Tools and Applications

Publication Date

1-1-2023

Abstract

A personality is a blend of an individual’s psychological characteristics and qualities, displaying human behaviour. Recently, the development of computational models for personality recognition has received research scientists’ attention. Prior studies on personality trait prediction have used machine and deep learning techniques, which perform feature extraction but do not retain long-term dependencies. In this study, we apply a deep learning model, namely BiLSTM, that can maintain long-term dependencies in both forward and backward directions for personality prediction on a benchmark essay dataset. The suggested model outperforms current strategies in classifying the user’s personality attributes. With this research’s findings, firms may make better judgments about hiring personnel. They may also use the research findings to choose, manage, and optimize their strategies, activities, and commodities.

ISSN

1380-7501

Publisher

Springer Science and Business Media LLC

Disciplines

Computer Sciences

Keywords

BiLSTM, Deep learning, Extravert, Introvert, Personality recognition

Scopus ID

85169928460

Indexed in Scopus

yes

Open Access

no

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