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Title: Recent trends in deep learning based personality detection
Authors: Mehta, Yash
Majumder, Navonil
Gelbukh, Alexander
Cambria, Erik
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Mehta, Y., Majumder, N., Gelbukh, A. & Cambria, E. (2020). Recent trends in deep learning based personality detection. Artificial Intelligence Review, 53(4), 2313-2339.
Journal: Artificial Intelligence Review
Abstract: Recently, the automatic prediction of personality traits has received a lot of attention. Specifically, personality trait prediction from multimodal data has emerged as a hot topic within the field of affective computing. In this paper, we review significant machine learning models which have been employed for personality detection, with an emphasis on deep learning-based methods. This review paper provides an overview of the most popular approaches to automated personality detection, various computational datasets, its industrial applications, and state-of-the-art machine learning models for personality detection with specific focus on multimodal approaches. Personality detection is a very broad and diverse topic: this survey only focuses on computational approaches and leaves out psychological studies on personality detection.
ISSN: 0269-2821
DOI: 10.1007/s10462-019-09770-z
Schools: School of Computer Science and Engineering 
Rights: © 2019 Springer Nature B.V. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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