Survey on personality detection using deep learning techniques
TL;DR: In this paper, the authors used deep learning methods to perform personality detection; however, it can provide activation functions called sigmoid, tan h, and leaky ReLU.
Abstract: Detection of the personality involves the extraction of behavioral traits, feelings, motives, and ideas. Many people get much knowledge across their social networks. This highly necessitates the need to derive the personality characteristics from some of the documents, photos, videos. Certain human personality characteristics are used to detect text details, such as paragraphs, essays. Some applications are undertaken, such as Forensic, Mental Wellbeing, Diagnostic, etc. The deep learning methods are used to perform personality detection; however, it can provide activation functions called sigmoid, tan h, and leaky ReLU. Here are text documents analyzed by convolutionary neural networks and current neural networks. Some of the human activity characters are also investigated in this study. Centered on this, this research has improved efficiency and precision in the recognition of personality.
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02 Sep 2018
TL;DR: This study proposes two deep learning structures for the task of personality recognition using acoustic-prosodic, psycholinguistic, and lexical features, and presents empirical results of several experimental configurations, including a cross-corpus condition to evaluate robustness.
Abstract: Deep learning has been very successful on labeling tasks such as image classification and neural network modeling, but there has not yet been much work on using deep learning for automatic personality recognition. In this study, we propose two deep learning structures for the task of personality recognition using acoustic-prosodic, psycholinguistic, and lexical features, and present empirical results of several experimental configurations, including a cross-corpus condition to evaluate robustness. Our best models match or outperform state-of-theart on the well-known myPersonality corpus, and also set a new state-of-the-art performance on the more difficult CXD corpus.
20 citations
01 Dec 2018
TL;DR: This work exploited recent research in text analysis and personality detection to build an automatic brand personality prediction model on top of the (Five-Factor Model) and (Linguistic Inquiry and Word Count) features extracted from publicly available benchmarks that reported significant accuracy in predicting specific personality traits form brands.
Abstract: User-generated content on social media platforms is a rich source of latent information about individual variables. Crawling and analyzing this content provides a new approach for enterprises to personalize services and put forward product recommendations. In the past few years, brands made a gradual appearance on social media platforms for advertisement, customers support and public relation purposes and by now it became a necessity throughout all branches. This online identity can be represented as a brand personality that reflects how a brand is perceived by its customers. We exploited recent research in text analysis and personality detection to build an automatic brand personality prediction model on top of the (Five-Factor Model) and (Linguistic Inquiry and Word Count) features extracted from publicly available benchmarks. The proposed model reported significant accuracy in predicting specific personality traits form brands. For evaluating our prediction results on actual brands, we crawled the Facebook API for 100k posts from the most valuable brands' pages in the USA and we visualize exemplars of comparison results and present suggestions for future directions.
11 citations