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Recent Trends in Deep Learning Based Personality Detection
Yash Mehta, Navonil Majumder, Alexander Gelbukh, Erik Cambria
TL;DR
Automated personality detection lacks a recent comprehensive perspective despite growing interest, especially in multimodal affective computing. This paper reviews computational datasets, applications, and machine-learning approaches with emphasis on deep learning. The review finds that deep learning and multimodal fusion have achieved leading performance, while larger, more diverse datasets and ethical safeguards remain important boundaries.
Problem
Automated personality detection spans diverse computational methods and modalities, but the field needs broader evidence and better datasets for reliable development.
Method
The paper reviews machine-learning-based personality detection across text, audio, video, and multimodal inputs, emphasizing deep-learning models, datasets, and applications.
Results
Deep learning with multimodal fusion represents the state of the art, and combining multiple modalities often produces higher prediction accuracy than unimodal approaches.
Takeaways & Limitations
The field is moving from text-only detection toward visual and multimodal systems, with potential integration into human–machine interaction agents.
Takeaways & Limitations
The field lacks enough large, accurate, and diverse labeled datasets, while political deployment raises risks involving personal data and manipulation.
Abstract
from arXiv · showhide
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.
1 Introduction
The paper introduces automated personality detection as a practically significant computational task and reviews personality models, applications, and ethical concerns. It emphasizes the widespread Big-Five framework and the potential for personalized human–computer interactions.
- Personality combines behavior, emotion, motivation, and thought patterns, influencing life choices, well-being, health, preferences, and desires.
- The Big-Five traits are the literature’s dominant personality measure, while alternative schemes include 16PF, EPQ-R, PEN, and MBTI.
- Computational personality detection may support devices that adapt responses and personalities to different people and situations.
- Automated personality recognition is proposed for personalized assistants, recommendation systems, sentiment interpretation, health care, forensics, and job screening.
- Ethical concerns include privacy invasion, cultural bias, confidentiality, and inappropriate use or interpretation of personality tests.
- Political use of personality detection raises concerns about exploiting personal data to identify voters susceptible to targeted propaganda.
- Machine-learning systems may inherit biases from personality tests whose accuracy lacks valid proof, motivating transparency and assessment procedures such as Algorithmic Impact Assessments.
2 Related Works
The paper addresses a lack of recent comprehensive perspectives on automated personality detection by surveying developments across modalities and related research areas. It positions the review as a broad overview of recent trends, particularly after deep neural networks began surpassing earlier methods.
- The survey presents itself as an early comprehensive bird’s-eye view of recent trends and developments in automated personality detection.
- It responds to the absence of a recent overall perspective on advances in machine-learning-based automated personality detection.
- After 2014, end-to-end deep neural architectures began beating state-of-the-art accuracies achieved by earlier shallow text-learning methods.
- The related literature covers text, visual features, social media, non-verbal behavior, personality recognition, and personality perception.
3 Baseline Methods
The paper summarizes commonly used baseline models and feature-extraction techniques for text, audio, visual, and multimodal personality detection. These approaches range from handcrafted modality-specific features with standard classifiers to deep CNNs and fusion strategies.
- Text systems extract LIWC, Mairesse, and MRC features for classifiers including SMO, SVM, and Naïve Bayes, while newer models learn word embeddings.
- Audio detection commonly uses MFCC, ZCR, Logfbank, and other cepstral or spectral features with SVMs and linear regressors.
- Deep-learning personality detection commonly uses CNNs, especially for visual inputs, where facial features are mapped to personality traits.
- Visual systems extract facial information through EigenFace, HOG, FACS, Viola–Jones, and pretrained networks such as VGG-Face with transfer learning.
- Multimodal methods use early fusion by combining extracted modality features or late fusion by averaging separate audio and visual predictions.
- Deep bimodal regression using modified Deep Residual Networks is reported to achieve state-of-the-art results.
4 Detailed Overview
The reviewed studies use textual, audio, visual, and multimodal signals for personality detection, with multimodal inputs often improving prediction accuracy. Research also emphasizes interpretable visual models, perceived personality, and applications beyond personality prediction.
- Multimodal Personality Detection: Combining inputs from more than one modality often results in higher prediction accuracy, while visual features are generally most accurate for unimodal detection.The review contrasts unimodal visual performance with multimodal gains across the analyzed studies.
- Text Modality: Closed-vocabulary text tools such as Receptiviti can perform poorly on social-media micro-text because predefined word categories limit accuracy.Receptiviti analyzes text through psycholinguistic features derived from LIWC.
- Text Modality: Personality predictions have been derived from social behavior, grammatical patterns, commonsense knowledge, and psycholinguistic features in social-media text.Combining commonsense knowledge with psycholinguistic features produced a reported remarkable improvement in accuracy.
- Text Modality: Deep text models represent personality using hierarchical word and sentence structures, including character-based and bi-RNN GRU representations instead of standard embeddings.These approaches target language-independent or deep representations of textual personality cues.
- Audio and Conversational Modality: Conversation-based personality detection is harder than simple-text analysis because dyadic interactions require modeling temporal dependencies and mutual influence during turn-taking.Recurrent networks are used to model language dependencies in conversational settings.
- Audio and Conversational Modality: Audio studies compare non-linguistic cues such as prosody, overlaps, interruptions, and speech activity with linguistic features for perceived personality detection.Work using the AMI meeting corpus incorporates dialog-act tags alongside conversational signals.
- Visual Modality: Visual studies find correlations between personality and profile-picture aesthetics, facial posture, expressions, and action units, with linear AU classifiers achieving accuracy close to the state of the art.CNN analyses also use class activation maps to interpret internal representations and identify high-level facial features.
- Visual Modality: Segment-level occlusion analysis shows that different image regions modulate different traits, including effects from background, skin, mouth, and eyes.The findings indicate that both facial and background regions contain information about perceived personality traits.
5 Results and Discussions
The review finds that deep learning combined with multimodal feature fusion represents the current state of the art in personality detection. It also identifies expanding datasets and applications, while noting gaps across personality measures and the need for more reliable models.
- The CVPR First Impressions dataset is the most popular multimodal dataset, while resources remain uneven across personality measures.Big-Five datasets are plentiful, but MBTI, PEN, and 16PF resources are comparatively lacking.
- The field’s growth is supported by publicly available annotated datasets, allowing research to focus more on models and architectures than data procurement and preprocessing.
- MBTI traits are harder and more complex to predict than Big-Five traits, although deep learning models are beginning to achieve good accuracy for MBTI.
- Deep learning with multimodal feature fusion has achieved the state of the art in personality detection.Bimodal fusion is especially common, while trimodal fusion remains relatively underexplored.
- More reliable personality prediction could support integration into human–machine interaction systems such as voice assistants, robots, and cars.The review also anticipates industrial implementation of the discussed deep learning models and continued demand for datasets covering varied personality measures.
6 Conclusion
Automated personality detection has promising applications, but progress is constrained by limited, insufficiently diverse training data and uncertain labeling quality. Recent multimodal deep learning methods perform well and are beginning to produce reliable predictions.
- The field needs larger, more accurate, and more diverse datasets because many cases lack enough labeled data to train large neural networks.
- Current datasets predominantly use the Big-Five model, with relatively few datasets covering MBTI or PEN measures.
- Survey responses may not provide fully credible personality labels even when participants answer honestly, motivating more accurate and efficient labeling methods.
- Recent multimodal deep learning techniques perform well and are beginning to make reliable personality predictions.
- Future architectures are expected to use deeper models and efficient multimodal fusion to map complex functions for personality detection.