Depression Detection Using Sentiment Analysis

This dataset is a collection of mental health statuses, gathered from real statements shared by individuals. By bringing together and refining data from various sources, it provides a rich and reliable foundation for developing chatbots and exploring sentiment analysis. The goal is to offer meaningful insights and create tools that can better understand and respond to mental health needs.

The dataset brings together information from the following Kaggle datasets:
• 3k Conversations Dataset for Chatbot
• Depression Reddit Cleaned
• Human Stress Prediction
• Predicting Anxiety in Mental Health Data
• Mental Health Dataset Bipolar
• Reddit Mental Health Data
• Students Anxiety and Depression Dataset
• Suicidal Mental Health Dataset
• Suicidal Tweet Detection Dataset

Dataset Overview:

The data is collected from various platforms, including social media, Reddit, Twitter, and others. Each entry is labeled with a specific mental health status. The dataset contains statements categorized under one of the following seven mental health statuses:
Normal

1. Depression
2. Suicidal
3. Anxiety
4. Stress
5. Bipolar
6. Personality Disorder

Features:

1. A unique identifier for each entry
2. Statement: The textual data or post
3. Status: The labeled mental health status of each statement

Usage:

The dataset's structure and features make it highly versatile for various applications, particularly in the fields of mental health, artificial intelligence, and data analysis. Here are some key use cases:

1. Mental health chatbot development
2. Sentiment and emotion analysis
3. Early detection and intervention
4. Academic and clinical research
5. Social media monitoring
6. NLP model training

Acknowledgments:

This dataset is created by gathering and refining data from publicly available Kaggle datasets. Special thanks to the original dataset creators for their valuable contributions.