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README.md

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+# Mental Health Sentiment Analysis
+
+## Table of Contents
+* [Background](#background)
+* [Requirement](#requirement)
+* [Inspiration](#inspiration)
+* [Schema](#schema)
+* [Creator](#creator)
+
+## Background
+<img src="https://github.com/Bayunova28/Mental_Health_Sentiment_Analysis/blob/main/background.jpg" height="600" width="1100">
+<p align="justify">Mental health problems affect society as a whole, and not just a small, isolated segment. They are therefore a major challenge to global development. No group is immune to mental disorders,
+but the risk is higher among the poor, homeless, the unemployed, persons with low education, victims of violence, migrants and refugees, indigenous populations, children and adolescents, abused women 
+and the neglected elderly.</p>
+
+## BERT (Bidirectional Encoder Representations from Transformers)
+<img src="https://github.com/Bayunova28/Mental_Health_Sentiment_Analysis/blob/main/BERT_model.png" height="600" width="1100">
+<p align="justify">BERT, short for Bidirectional Encoder Representations from Transformers, is a Machine Learning (ML) model for natural language processing. It was developed in 2018 by researchers at Google AI Language and 
+  serves as a swiss army knife solution to 11+ of the most common language tasks, such as sentiment analysis and named entity recognition.</p>
+
+## Acknowledgement
+* [Investing in MENTAL HEALTH](https://iris.who.int/bitstream/handle/10665/42823/9241562579.pdf?sequence=1)
+* [BERT 101 🤗 State Of The Art NLP Model Explained](https://huggingface.co/blog/bert-101)
+* Data Source: [Sentiment Analysis for Mental Health](https://www.kaggle.com/datasets/suchintikasarkar/sentiment-analysis-for-mental-health)