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https://hdl.handle.net/10356/155153
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. https://dx.doi.org/10.1007/s10462-019-09770-z | 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. | URI: | https://hdl.handle.net/10356/155153 | 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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