Stress is a common and often debilitating experience that can have negative impacts on an individual's physical and mental health. In recent years, social media platforms have become an important tool for individuals to connect with others, share their thoughts, opinions, emotions, and experiences, and seek support during times of stress. Stress analysis on social media has garnered significant attention as a means of understanding the mental health and well-being of individuals. Sentiment Analysis (SA), Machine learning (ML), and Deep Learning (DL) techniques have been widely applied to various fields in recent years, including the analysis of social media data. In particular, stress analysis on social media has received increasing attention due to the growing recognition of the impact of stress on mental health. By applying SA, ML, and DL techniques to social media data, it is possible to understand the emotional state of individuals better and identify potential stressors that may be affecting their mental health. This study aims to develop a stress detection method encompassing SA and classification techniques. The classification techniques are Logistic Regression (LR), Decision Tree (DT), Random Forest (RF), and Convolutional Neural Networks (CNN). The method automats text and features analysis of social media posts and detects stress in social media data. The Reddit Stress Analysis in Social Media dataset has been used to test the proposed method. The test result reveals an accuracy metric of 85.32% for LR, 90.07% for DT, 95.18% for RF, and 93.50% for CNN. The resulting Sentiment Analysis and Classification (SA-C)-based stress detection method with RF model improves extracting stress patterns in social media data.