Alsini Raed, Naz Anam, Khan Hikmat Ullah, Bukhari Amal, Daud Ali, Ramzan Muhammad
Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
Department of Information Technology, University of Sargodha, Sargodha, Punjab, Pakistan.
Sci Rep. 2024 Dec 2;14(1):29875. doi: 10.1038/s41598-024-81506-8.
The latest advancements of deep learning have resulted in a new era of natural language processing. The machines now possess an unparallel ability to interpret and engage with various tasks such as text classification, content generation and natural language understanding. This development extended to the analysis of human behavior, where deep learning models are used to decode human personality. Due to the rise of social media, generating huge amounts of textual data that reshaped communication patterns. Understanding personality traits is a challenging topic which helps us to explore the patterns of thoughts, feelings and behaviors which are helpful for recruitment, career counselling and consumers' behavior for marketing, etc. In this research study, the main aim is to predict the human personality trait of agreeableness showing whether a person is emotional who feels a lot or thinker who is logical and has rational thinking. This behavior leads to analyzing them as cooperative, friendly and respecting difference of views. For comprehensive empirical analysis, shallow machine learning models, ensemble models, and deep learning technique including state of the art transformer-based models are applied on real-world dataset of MBTI. For feature engineering, textual features of TF-IDF and POS tagging and word embeddings such as word2vec, glove and sentence embeddings are explored. The results analysis shows the highest performance 91.57% with sentence embeddings utilizing Bi-LSTM algorithm that highlights the power of this study as compared to existing studies in the relevant literature.
深度学习的最新进展开启了自然语言处理的新时代。如今，机器在解释和处理各种任务（如文本分类、内容生成和自然语言理解）方面拥有无与伦比的能力。这一发展延伸到了对人类行为的分析，深度学习模型被用于解读人类性格。由于社交媒体的兴起，产生了海量的文本数据，重塑了交流模式。理解人格特质是一个具有挑战性的话题，它有助于我们探索思想、情感和行为模式，这对招聘、职业咨询以及营销中的消费者行为分析等都有帮助。在本研究中，主要目标是预测宜人性这一人格特质，即判断一个人是情感丰富、感受强烈的人，还是逻辑清晰、理性思考的思考者。这种行为表现为合作、友好且尊重不同观点。为了进行全面的实证分析，将浅层机器学习模型、集成模型以及包括基于最先进的Transformer模型在内的深度学习技术应用于MBTI的真实世界数据集。对于特征工程，探索了TF-IDF和词性标注等文本特征以及诸如word2vec、glove等词嵌入和句子嵌入。结果分析表明，使用双向长短期记忆（Bi-LSTM）算法的句子嵌入取得了91.57%的最高性能，与相关文献中的现有研究相比，突出了本研究的优势。
