Paper Title
Personality Analysis Using Deep Learning Methods

Abstract
Language serves as a primary and reliable conduit for individuals to express their deepest thoughts and emotions. Enhancing the modeling of user-generated text posts, which inherently encapsulate various facets of the author’s personality, holds significant promise for advancing the efficacy of personality and behavior analysis methodologies. Historically, attempts have been made to leverage Support Vector Machines (SVM) and Latent Dirichlet Allocation (LDA) with syntactic and lexical data to deduce individuals’ personality traits from publicly available content on online social platforms. However, the impact of these approaches is constrained by the limited size of the dataset used and the sensitivity of their perfor- mance to the chosen data representation. In this paper, we adopt contemporary deep learning techniques, specifically employing Bidirectional Encoder Representations from Transformers (BERT), to conduct personality analyses of users based on MBTI personality types. Our objective is to surpass the performance of existing methods by harnessing recent algorithms and integrating cross-lingual datasets into our analytical framework. Keywords - Deep Learning, Personality Analysis, BERT Fine-Tuning. Transformers