Personality traits analysis using Artificial Neural Networks: A Literature Survey

Remaida, Ahmed; Abdellaoui, Benyoussef; Moumen, Aniss; Idrissi, Younès El Bouzekri El

doi:10.1109/iraset48871.2020.9092076

Cited by 13 publications

(1 citation statement)

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“…Table 1 shows a list of common personality trait models. Within each personality model, different human personalities are distinguished according to psychological theories, the most popular measure in automatic personality detection being the use of the Big Five model [24]. Traditionally, personality traits were mostly determined by questionnaire analysis and selfassessment to determine an individual's personality score: these can effectively and directly present an individual's true personality; however, there are still limitations that make it difficult to explore more complex behavioral patterns in depth [25,26].…”

Section: Related Workmentioning

confidence: 99%

A Multimodal Affective Sensing Model for Constructing a Personality-Based Financial Advisor System

Lee

Yang²,

Su³

et al. 2022

Applied Sciences

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To achieve successful investments, in addition to financial expertise and knowledge of market information, a further critical factor is an individual’s personality. Decisive people tend to be able to quickly judge when to invest, while calm people can analyze the current situation more carefully and make appropriate decisions. Therefore, in this study, we developed a multimodal personality-recognition system to understand investors’ personality traits. The system analyzes the personality traits of investors when they share their investment experiences and plans, allowing them to understand their own personality traits before investing. To perform system functions, we collected digital human behavior data through video-recording devices and extracted human behavior features using video, speech, and text data. We then used data fusion to fuse human behavior features from heterogeneous data to address the problem of learning only one-sided information from a single modality. Through several experiments, we demonstrated that multimodal (i.e., three different signal inputs) personality trait analysis is more accurate than unimodal models. We also used statistical methods and questionnaires to evaluate the correlation between the investor’s personality traits and risk tolerance. It was found that investors with higher openness, extraversion, and lower neuroticism personality traits took higher risks, which is similar to research findings in the field of behavioral finance. Experimental results show that, in a case study, our multimodal personality prediction system exhibits high performance with highly accurate prediction scores in various metrics.

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Section: Related Workmentioning

confidence: 99%