The problem Stress Detection with Machine Learning solves

Stress, anxiety, and depression are threatening the mental health of people. Every person has a reason for having a stressful life. People often share their feelings on social media platforms like on Instagram in the form of posts and stories, and on Reddit in the form of asking for suggestions about their life on subreddits. In the past few years, many content creators have come forward to create content to help people with their mental health. Many organizations can use stress detection to find which social media users are stressed to help them quickly. So if you want to learn how to use machine learning to detect stress on social media posts, this article is for you. In this article, I will take you through the task of stress detection with machine learning using Python.

While looking for datasets that I can use to train a machine learning model for stress detection, I found a dataset on Kaggle with 116 columns. We only need to use the text and label column for this task.

The dataset I am using for this task contains data posted on subreddits related to mental health. This dataset contains various mental health problems shared by people about their life. Fortunately, this dataset is labelled as 0 and 1, where 0 indicates no stress and 1 indicates stress. So in the section below, I will take you through the task of stress detection in social media posts using Python.

Challenges I ran into

Nothing such a big issue is coming in front of me .