This paper presents a hybrid deep learning approach for personality trait classification from textual data. With the rapid growth of social media platforms, analyzing personality traits from user-generated text has become an important research area in natural language processing. The proposed system combines Convolutional Neural Networks (CNN) for effective feature extraction and Long Short-Term Memory (LSTM) networks for capturing contextual and sequential dependencies in text. The model utilizes TF-IDF for feature representation and is trained and evaluated on a labeled personality dataset based on standard personality traits. Extensive preprocessing techniques, including text cleaning, tokenization, and normalization, are applied to improve data quality and model performance. Experimental results show that the proposed CNN–LSTM model achieves an accuracy of 98%, outperforming traditional machine learning models such as Support Vector Machine (56%), Random Forest (53%), and K-Nearest Neighbors (31%). The improved performance of the hybrid model is attributed to its ability to learn both local semantic features and long-term contextual relationships in textual data. Furthermore, the model demonstrates strong generalization capability and robustness when applied to unseen data. The results indicate that the proposed approach is highly effective for real-world applications such as personalized recommendation systems, mental health analysis, user behavior prediction, and human-computer interaction.

Keywords — Personality Trait Classification; Deep Learning;

CNN-LSTM; Natural Language Processing; Text Mining; Machine Learning.