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Machine and deep learning for personality traits detection: a comprehensive survey and open research challenges

ARTIFICIAL INTELLIGENCE REVIEW 2025年第8期58卷 1-57页

作者： Naz, Anam Khan, Hikmat Ullah Bukhari, Amal Alshemaimri, Bader Daud, Ali Ramzan, Muhammad Univ Sargodha Dept Comp Sci Sargodha Punjab Pakistan Univ Sargodha Dept Informat Technol Sargodha Punjab Pakistan Univ Jeddah Dept Informat Syst & Technol Collage Comp Sci & Engn Jeddah Saudi Arabia King Saud Univ Coll Comp & Informat Sci Software Engn Dept Riyadh Saudi Arabia Rabdan Acad Fac Resilience Abu Dhabi U Arab Emirates Univ Sargodha Dept Software Engn Sargodha Punjab Pakistan

natural language processing (NLP), a prominent research domain of Artificial Intelligence (AI), analyzes users' generated content on social media for various purposes like sentiment analysis, text summarization, chatbots, fake news detection, etc. Recent advancements in NLP have helped for analysis of human behavior analysis and predicting various human personality traits. Understanding personality traits has long been a fundamental pursuit in psychology and cognitive sciences due to its vast applications for understanding from individuals to social dynamics. Due to online social platforms where people express their views, experiences and comments, NLP is applied for users' behavior and personality analysis, which is helpful in defining marketing strategies, consumers' behavior analysis, team building, etc. This research study provides a comprehensive overview of existing methodologies, applications, and challenges in the field of personality traits detection using shallow machine learning, ensemble learning and deep learning. To conduct this study, recent research publications relevant to NLP for this new but emerging research domain are reviewed. The background knowledge of personality models of various nature is discussed for better domain understanding. The study encompasses machine learning and deep learning models with thorough analysis of traditional and innovative techniques including ensemble learning and transformer-based models in chronological order highlighting the trend analysis showing evolution of application of advanced methods. The review also presents and compares the widely used datasets which may guide the researchers for selection of datasets in future studies. Performance evaluation metrics have been discussed which are used in the relevant literature. Furthermore, it explores the application of research of personality traits detection in various domains highlighting its significance. We have also carried out extensive empirical analysis using

关键词： Artificial intelligence Machine learning Personality traits Data analytics Psychological analysis natural language processing

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Advancing Gender Equality in Media: Tackling Stereotypes and Biases with AI 16th

Advancing Gender Equality in Media: Tackling Stereotypes and...

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16th International KES conference on Intelligent Decision Technologies

作者： Liu, Zhan Darbellay, Anne Balet, Nicole Glassey Vuille, Valerie Univ Appl Sci & Arts Western Switzerland Media Innovat Lab HES SO Valais Wallis Delemont Switzerland DecadreE Geneva Switzerland

ISBN: (纸本)9789819774210;9789819774197;9789819774180

This study presents an innovative approach to evaluating media representations of gender-based violence by integrating natural language processing (NLP) techniques with the advanced capabilities of GPT-4, an Artificial Intelligence (AI)based large language model. We developed a set of 27 expert-defined criteria to analyze a corpus of news articles, initially utilizing NLP methods for foundational text analysis. For more complex criteria, we employed GPT-4 and further enhanced its precision with fine-tuning. Our results indicate a significant increase in accuracy, achieving an overall 76% accuracy rate in content evaluation, which is 9% points higher than using NLP alone. This research introduces a novel media content analysis framework and paves the way for future enhancements in automated journalism assessment and ethical reporting.

关键词： Artificial intelligence Automated evaluation Gender-based violence GPT-4 Media content analysis natural language processing

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MLEE: Event Extraction as Multi-label Classification Task at Token Level 6th

MLEE: Event Extraction as Multi-label Classification Task a...

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6th IFIP TC 12 International conference on Intelligence Science, ICIS 2024

作者： Yang, Jinshun Huang, Shuangxi Huang, Mingfeng Tsinghua University Beijing China

ISBN: (纸本)9783031712524

Event Extraction is an important task in natural language understanding, which aims to identify event trigger of pre-defined event types and their arguments of specific roles, has attracted a lot of attention from industry and academia. The previous works failed to address some issues, including error propagation problem, overlap and nest problem, and high complexity of model. This work proposes a novel model MLEE, which models Event Extraction task as Multi-Label classification task at token level, and processes the extraction task in a joint paradigm, can help solving issues mentioned above. The experiment verifies our model’s effectiveness. empirical results on DuEE and FewFC shows that MLEE outperforms previous best model, pushing trigger extraction F1 to 85.03% (+4.45%), argument extraction F1 to 78.85% (+2.72%) on DuEE, pushing trigger extraction F1 to 76.69% (+1.63%), argument extraction F1 to 76.53% (+5.27%) on FewFC. © IFIP International Federation for Information processing 2025.

关键词： natural language processing systems

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Research on the Application of Intelligent Writing Teaching System Based on Computer Vision and natural language processing in Higher Vocational English 24

Research on the Application of Intelligent Writing Teaching ...

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5th International conference on Computer Science and Management Technology, ICCSMT 2024

作者： Wen, Liuchun Guangdong Polytechnic of Water Resources and Electric Engineering Guangdong Guangzhou China

ISBN: (纸本)9798400709999

In the context of vocational English teaching, writing skills are crucial for students' professional communication ***, traditional teaching methods often face challenges such as delayed feedback and insufficient *** the development of artificial intelligence technologies, the integration of computer vision (CV) and natural language processing (NLP) into writing education provides a novel solution to these *** study explores the application of a CV- and NLP-based intelligent writing assistance system in vocational English writing *** system enhances grammar correction, semantic understanding, and automated feedback, significantly improving students' writing performance while reducing teachers' *** experimental evaluation demonstrated that the system effectively improved writing accuracy, logical clarity, and overall learning *** findings indicate that AI-driven educational tools have great potential in transforming traditional language teaching practices. © 2024 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Apprentices

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The Role of natural language processing Tasks in Automatic Literary Character Network Construction 31

The Role of Natural Language Processing Tasks in Automatic L...

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31st International conference on Computational Linguistics, COLING 2025

作者： Amalvy, Arthur Labatut, Vincent Dufour, Richard Laboratoire Informatique d'Avignon France Laboratoire des Sciences Du Numérique de Nantes France

ISBN: (纸本)9798891761964

The automatic extraction of character networks from literary texts is generally carried out using natural language processing (NLP) cascading pipelines. While this approach is widespread, no study exists on the impact of low-level NLP tasks on their performance. In this article, we conduct such a study on a literary dataset, focusing on the role of named entity recognition (NER) and coreference resolution when extracting co-occurrence networks. To highlight the impact of these tasks' performance, we start with gold-standard annotations, progressively add uniformly distributed errors, and observe their impact in terms of character network quality. We demonstrate that NER performance depends on the tested novel and strongly affects character detection. We also show that NER-detected mentions alone miss a lot of character co-occurrences, and that coreference resolution is needed to prevent this. Finally, we present comparison points with 2 methods based on large language models (LLMs), including a fully end-to-end one, and show that these models are outperformed by traditional NLP pipelines in terms of recall. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Hierarchical Relation Distillation for Efficient 3D Visual Grounding

Hierarchical Relation Distillation for Efficient 3D Visual G...

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2025 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Liu, Jun School of Information Renmin University of China BeiJing China

ISBN: (纸本)9798350368741

3D Vision-language (3D-VL) Grounding seeks to localize 3D objects in point clouds using natural language descriptions. While existing methods focus on improving performance through intricate spatial understanding modules, the efficiency of 3D grounding models has been overlooked, despite its importance for real-time applications. To address this issue, we propose a novel hierarchical relation distillation framework that creates a small yet high-performing model through a two-stage distillation process. The first stage focuses on semantic feature distillation, eliminating noise and retaining valuable information for second stage training. The second stage utilizes half-layers of the teacher model for 3D relation feature distillation, enhancing computational efficiency without sacrificing accuracy. Experimental results on the challenging Nr3D and Sr3D datasets demonstrate that our distilled model achieves state-of-the-art performance while reducing computational costs by over 40%. © 2025 IEEE.

关键词： 3D visual-language grounding Hierarchical relation distillation knowledge distillation

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Optimizing English Lexical Databases with BERT and Reinforcement Learning 5

Optimizing English Lexical Databases with BERT and Reinforce...

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5th IEEE International conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Saranya, V. Murthy, G.R.K. Alapati, Purnachandra Rao Mythili, M. Swarnamughi, K. Bala, B. Kiran Panimalar Engineering college Department of English Chennai India Narasaraopet India Prasad V Potluri Siddhartha Institute of Technology Department of English Andhra Pradesh India Department of English Erode India Sathyabama Institute of Science And Technology Department of English Chennai India K.Ramakrishnan College of Engineering Department of Ai & Ds Trichy India

ISBN: (纸本)9798350356236

The evolution of natural language processing (NLP) has significantly transformed the way this study analyzes and understand language. This study presents a novel approach to generating lexical databases for English language sources using AI-powered methods, specifically leveraging BERT and reinforcement learning. Traditional methods for lexical database creation often rely on manual compilation or semi-automated techniques, which can be time-consuming and may lack comprehensive coverage of linguistic nuances. These existing approaches struggle with scalability and adaptability, particularly when addressing evolving language use and the inclusion of rare or domain-specific terms. The Open English WordNet dataset serves as a foundational resource, providing rich lexical relationships and contextual information necessary for effective database generation. Our proposed method integrates BERT for context-aware embedding generation and reinforcement learning for continuous optimization of lexical extraction and enrichment processes. This innovative combination not only enhances the accuracy of detecting synonyms, antonyms, hypernyms, and partof-speech tags but also ensures that the system learns from user feedback, leading to ongoing refinement. The advantages of this method include improved precision and recall, rapid processing times, and the ability to adapt to new linguistic data dynamically. Ultimately, this study demonstrates that AI-driven approaches can significantly enhance the quality and usability of lexical databases, paving the way for more sophisticated applications in natural language processing and linguistics. © 2025 IEEE.

关键词： natural language processing systems

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Training-Free Task Planning by Parsing language Signals With Common Sense

Training-Free Task Planning by Parsing Language Signals With...

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2025 IEEE International conference on Acoustics, Speech, and Signal processing, ICASSP 2025

作者： Zhang, Xianqi Wang, Wenrui Chai, Shitong Wang, Xingtao Fan, Xiaopeng Faculty of Computing Harbin Institute of Technology Harbin150001 China Peng Cheng Laboratory Shenzhen518000 China

ISBN: (纸本)9798350368741

Task planning refers to autonomously organizing actions in response to instruction signals, especially language signals. Previous reinforcement learning and imitation learning methods always require a large amount of task-related data (data interacting with the environment or expert demonstrations) for policy training. In this paper, we propose a method named language Signal Parse Tree (LSPT for short) for task planning. LSPT is a training-free method and consists of a language signal parser and a precondition action generator. The language signal parser translates goal predicates (neural language instructions) into a predicate parse tree. This tree contains essential information about position and state changes, which is subsequently employed to generate key actions. The precondition action generator leverages commonsense to produce additional pre-actions that complement each key action, thereby increasing the likelihood of successful execution of all actions. The final plan used to accomplish the task incorporates both key actions and pre-actions. Experiments on a virtual indoor scene show that the proposed method outperforms the state-of-the-art method LID by 24.8% on In-Distribution dataset, 43.2% on Novel Scenes dataset, and 38.8% on Novel Tasks dataset. © 2025 IEEE.

关键词： common sense embodied agents language signal processing robots task planning

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Unsupervised Statistical Keyword Extraction Pipeline: Is LLM All You Need? 34th

Unsupervised Statistical Keyword Extraction Pipeline: Is LLM...

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34th Brazilian conference on Intelligent Systems

作者： Zagatti, Fernando Rezende Lucredio, Daniel Caseli, Helena de Medeiros Univ Fed Sao Carlos Dept Comp Sao Carlos Brazil

ISBN: (纸本)9783031790317;9783031790324

Keyword extraction is an important step for text interpretation, serving to identify and highlight the most significant words or phrases within a text. This step is essential for various applications such as summarization, indexing, and information retrieval. This paper presents a custom-built keyword extraction pipeline named USKE (Unsupervised Statistical Keyword Extraction) and compares its performance to large language models (LLMs). USKE is able to deliver fast and simple results based in statistical methods even when dealing with large datasets. Our evaluation demonstrates that although LLMs can achieve good results in single sentences with minimal context, they require a lot of post-processing and may output inconsistent answers, while USKE excels in efficiency and scalability.

关键词： Keyword extraction natural language processing Large language Models

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AI-Powered Mental Health Assessment using Emotion Detection for Real-Time Analysis

AI-Powered Mental Health Assessment using Emotion Detection ...

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2025 IEEE International conference on Computational, Communication and Information Technology, ICCCIT 2025

作者： Vijay, K. Raghakeerthana, R. Milinda, Renata Rachael Thusheel, S. Rajalakshmi Engineering College Department of Artificial Intelligence and Machine Learning Chennai India

ISBN: (纸本)9798331512965

This paper is an AI-driven mental health assessment tailored to each patient's distinct emotional needs, addressing the global mental health crisis. Using advanced machine learning and natural language processing, it aims to deliver personalized guidance for psychological and physical wellness activities, along with tools for tracking individual progress. The papers employ exploratory case studies and knowledge discovery techniques to evaluate the effectiveness of these interventions. Data mining methods reveal hidden patterns within mental health datasets, with the potential to reshape therapeutic paradigms. Expected outcomes include enhanced accessibility to mental health resources, early intervention, personalized progress monitoring, improved quality of life, and reduced stigma. By integrating sophisticated data processing, machine learning, and empathetic user interaction, it operates by redefining mental health support. The system's architecture is designed to promote both emotional and cognitive well-being, fostering positive change in mental health care delivery. © 2025 IEEE.

关键词： Computer Vision conversational bot Convolution neural network Emotion detection Machine learning natural language processing Real-Time Analysis

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