Mental Health Status Dataset for AI and Sentiment Analysis 🚀

Welcome to the Mental Health Status Dataset repository! This repository contains a meticulously curated dataset designed to support AI applications, sentiment analysis, and research in mental health.

---

🗂️ About the Dataset

This dataset is a compilation of mental health statuses derived from various textual statements. It has been cleaned and organized to serve as a valuable resource for:

• ✅ AI Chatbot Development: For building mental health support systems.
• ✅ Sentiment Analysis: To understand and analyze mental health trends.
• ✅ Research: Academic studies on mental health patterns and behavior.

🌟 Key Features:

Column Name	Description
unique_id	A unique identifier for each entry.
Statement	Textual data or post (e.g., social media content).
Mental Health Status	The tagged mental health status (one of 7 classes).

🧠 Mental Health Status Tags

The dataset includes seven categories of mental health statuses:

1. Normal
2. Depression
3. Suicidal
4. Anxiety
5. Stress
6. Bi-Polar
7. Personality Disorder

---

📈 Applications

This dataset is versatile and can be applied in:

• Training Machine Learning Models to predict mental health conditions.
• Building Intelligent Chatbots for mental health support.
• Academic Research: Understanding mental health patterns through sentiment analysis.

---

📚 Data Sources

The dataset is an aggregation of publicly available data from the following Kaggle sources:

• 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

Huge thanks to the original contributors for their invaluable work! 🙌

---

🚀 Getting Started

🔧 Installation and Setup

1. Clone the repository:

   git clone https://github.com/username/mental-health-status-dataset.git  
   cd mental-health-status-dataset  

2. Install the required dependencies:

   pip install -r requirements.txt  

📊 Exploratory Data Analysis (EDA)

• Analyze the distribution of mental health statuses.
• Visualize textual patterns and word clouds for insights.

🔧 Data Preprocessing

• Text cleaning: Tokenization, stemming, and stopword removal.
• Feature engineering for NLP-based tasks.

🤖 Model Training

• Train machine learning models like Logistic Regression, SVM, and Random Forest.
• Use deep learning models such as RNN, LSTM, and Transformers for advanced predictions.

🚀 Deployment

• Export trained models for deployment in real-world applications.
• Deploy models using Flask or FastAPI for AI chatbot integration.

---

📝 Code Implementation

Explore the full implementation, including:

1. EDA: Insights and visualizations 📊
2. Data Preprocessing: Clean and prepare data 🔧
3. Model Development: Train machine learning and deep learning models 🤖
4. Evaluation: Model performance metrics (accuracy, precision, recall, F1-score) 📈
5. Deployment: APIs for real-world integration 🚀

---

📄 Acknowledgments

This dataset was aggregated from publicly available sources on Kaggle. A big thank you to the original dataset creators for their contributions to the field of mental health.

---

💬 Contributing

We welcome contributions! Feel free to open issues or submit pull requests to enhance the dataset or improve the codebase.

---

📧 Contact

For any questions or collaborations, feel free to reach out:
📩 Email: arifmiahcse@gmail.com 🌐 LinkedIn: Your LinkedIn Profile

---

🌟 Show Your Support

If you find this dataset helpful, please ⭐ the repository and share it with others in your network. Together, we can make a difference in mental health research! 💙

---

Exploratory Data Analysis (EDA) Code

Here’s an example of EDA for the dataset:

# Import necessary libraries
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from wordcloud import WordCloud

# Load the dataset
df = pd.read_csv("mental_health_status_dataset.csv")

# Display basic information about the dataset
print("Dataset Shape:", df.shape)
print("\nDataset Info:")
print(df.info())
print("\nMissing Values:")
print(df.isnull().sum())

# Preview the dataset
print("\nDataset Head:")
print(df.head())

# Plot the distribution of mental health statuses
plt.figure(figsize=(10, 6))
sns.countplot(data=df, x="Mental Health Status", palette="viridis")
plt.title("Distribution of Mental Health Statuses", fontsize=16)
plt.xlabel("Mental Health Status")
plt.ylabel("Count")
plt.xticks(rotation=45)
plt.show()

# Generate a WordCloud for textual data (statements)
text = " ".join(df['Statement'].dropna())
wordcloud = WordCloud(width=800, height=400, background_color="white").generate(text)

plt.figure(figsize=(12, 6))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.title("WordCloud of Statements", fontsize=16)
plt.show()

---

Model Training Code

This code trains a basic Logistic Regression model to classify mental health statuses.

# Import necessary libraries
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report, accuracy_score, confusion_matrix
import seaborn as sns

# Clean the data (remove missing values)
df = df.dropna(subset=["Statement", "Mental Health Status"])

# Split the data into features and target
X = df["Statement"]
y = df["Mental Health Status"]

# Encode target labels
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
y = le.fit_transform(y)

# Split the data into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Convert text data to numerical features using TF-IDF
tfidf = TfidfVectorizer(max_features=5000, stop_words="english")
X_train_tfidf = tfidf.fit_transform(X_train)
X_test_tfidf = tfidf.transform(X_test)

# Train a Logistic Regression model
model = LogisticRegression(random_state=42, max_iter=1000)
model.fit(X_train_tfidf, y_train)

# Make predictions
y_pred = model.predict(X_test)

# Evaluate the model
print("Classification Report:")
print(classification_report(y_test, y_pred, target_names=le.classes_))

print("\nAccuracy Score:", accuracy_score(y_test, y_pred))

# Plot the Confusion Matrix
cm = confusion_matrix(y_test, y_pred)
plt.figure(figsize=(8, 6))
sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', xticklabels=le.classes_, yticklabels=le.classes_)
plt.title("Confusion Matrix", fontsize=16)
plt.xlabel("Predicted Label")
plt.ylabel("True Label")
plt.show()

---