README.md

---
license: cc-by-4.0
task_categories:
- text-classification
language:
- en
tags:
- text
- nlp
- classification
- suicide-prevention
- depression
- anxiety
- tabular
- mental-health
pretty_name: Mental Health Text Classification Dataset (4-Class)
size_categories:
- 10K<n<100K
configs:
- config_name: default
  data_files:
  - split: train
    path: "mental_heath_unbanlanced.csv"
  - split: test
    path: "mental_health_combined_test.csv"
  - split: features
    path: "mental_health_feature_engineered.csv"
---


# Mental Health Text Classification Dataset (4-Class)

## Dataset Description

This dataset contains short, user‑generated texts labeled for **4‑class mental health classification**: **Suicidal**, **Depression**, **Anxiety**, and **Normal**. It is a **derived dataset** created by combining and cleaning three public mental‑health corpora, then re‑labeling them into a unified 4‑class scheme and exporting CSV files suitable for both classical ML and modern NLP models.

The repository includes:

- An **unbalanced main training corpus** (realistic class skew).  
- A **strictly balanced test split** for fair evaluation.  
- A **feature‑engineered file** with basic text statistics (length, URLs, emojis, punctuation, etc.).

> **Important:** This dataset is intended for **research and education only**. It is not a clinical tool and must not be used for real‑world diagnosis, triage, or crisis intervention.

---

## Dataset Structure

### Files

1. **`mental_heath_unbanlanced.csv`**  
   - Main training corpus with **48,945 samples** and a realistic, unbalanced class distribution (Normal, Depression, Suicidal, Anxiety).
   - Columns:
     - `text` – Cleaned post or statement text.  
     - `status` – Final label in {`Suicidal`, `Depression`, `Anxiety`, `Normal`}.

2. **`mental_health_combined_test.csv`**  
   - **Balanced test split** with **992 samples** (exactly **248 per class**). 
   - Columns:
     - `text` – Cleaned post.  
     - `status` – Final 4‑class label.  
   - Built by combining held‑out data from the sources, applying the same cleaning and label mapping, removing duplicates, and **downsampling each class to equal size** for fair evaluation.
3. **`mental_health_feature_engineered.csv`**  
   - Cleaned and **feature‑engineered** subset for classical models and analysis. 
   - Columns:
     - Core: `Unique_ID`, `text`, `status` (4‑class label).  
     - Features:
       - `text_length` – Number of characters.  
       - `word_count` – Number of whitespace‑separated tokens.  
       - `num_urls` – Count of URL patterns.  
       - `num_emojis` – Count of emoji characters.  
       - `num_special_chars` – Count of non‑alphanumeric characters.  
       - `num_excess_punct` – Count of repeated punctuation sequences (e.g., `!!!`, `???`, `...`).  
       - `avg_word_length` – Average characters per word.

### Splits

This repo is organized at the **file level** (no HF `train`/`test` config baked in). A common convention is:

- `mental_heath_unbanlanced.csv` → `train` (and derive your own validation split).  
- `mental_health_combined_test.csv` → `test`.  

Example with `datasets`:

from datasets import load_dataset

ds_train = load_dataset(
"your-username/Mental-Health_Text-Classification_Dataset",
data_files={"train": "mental_heath_unbanlanced.csv"},
)

ds_test = load_dataset(
"your-username/Mental-Health_Text-Classification_Dataset",
data_files={"test": "mental_health_combined_test.csv"},
)

print(ds_train)
print(ds_test)

text

For the feature file:

ds_fe = load_dataset(
"your-username/Mental-Health_Text-Classification_Dataset",
data_files={"feature": "mental_health_feature_engineered.csv"},
)

text

---

## Source Data and Provenance

This dataset does **not** collect new data from individuals. Instead, it is built by **downloading, cleaning, and merging** three existing public resources.[file:64][file:65]

**Source datasets:**

- **Suicide and Depression Detection (Kaggle)** – Nikhileswar Komati  
  Reddit posts from suicide‑related communities labeled as suicidal vs non‑suicidal / depression vs control.  
  https://www.kaggle.com/datasets/nikhileswarkomati/suicide-watch

- **Sentiment Analysis for Mental Health (Kaggle)** – Suchintika Sarkar  
  Short statements labeled as normal, depression, suicide, anxiety, stress, bipolarity, personality disorder, etc.  
  https://www.kaggle.com/datasets/suchintikasarkar/sentiment-analysis-for-mental-health

- **Reddit Mental Health Classification (Murarka et al.)**  
  Dataset released with *“Detection and Classification of Mental Illnesses on Social Media using RoBERTa.”*  
  Reddit posts labeled into ADHD, Anxiety, Bipolar, Depression, PTSD, and None.  
  https://github.com/amurark/mental-health-detection

Only the **processed, relabeled, and re‑split** samples are redistributed here. Users must obtain the original raw data from the links above if needed.

---

## Preprocessing and Label Mapping

### Cleaning

All source datasets are passed through a common cleaning pipeline:

- Drop rows with missing `text` or labels (`status`).  
- Deduplicate texts across and within sources.  
- Apply regex‑based text normalization (whitespace cleanup, formatting fixes).  
- Filter out extremely short and excessively long texts to remove non‑informative samples.

### Unified 4‑Class Labels

Original labels vary widely (suicide vs non, multiple disorders, control). They are mapped to a **four‑class scheme**:

- **Suicidal** – Posts explicitly labeled as suicide / suicidal ideation.  
- **Depression** – Posts labeled as depression / depressed.  
- **Anxiety** – Posts labeled with anxiety‑related categories.  
- **Normal** – Control / non‑suicide / normal posts.

Samples whose labels cannot be clearly mapped (e.g., stress, bipolar, personality disorder, PTSD, mixed or ambiguous labels) are **discarded** to keep the final label space clean.

### Splits and Balance

- The **main training file** (`mental_heath_unbanlanced.csv`) preserves the **natural class imbalance** after cleaning and mapping. 
- The **balanced test file** (`mental_health_combined_test.csv`) is created by:
  - Building a held‑out pool from the sources.  
  - Applying the same cleaning and label mapping.  
  - Removing duplicates.  
  - **Downsampling each class to 248 samples** for a total of 992 rows.

The feature‑engineered file is generated from the cleaned sentiment subset by adding the numerical text features listed above.

---

## Intended Uses

### Primary Uses

- Research and teaching on **mental‑health‑related text classification**.  
- Benchmarking binary or multi‑class classifiers (especially 4‑class).  
- Studying the impact of **class imbalance**, basic feature engineering, and model robustness on noisy social‑media text.

Typical tasks:

- 4‑way text classification: Suicidal vs Depression vs Anxiety vs Normal.  
- Ablation studies: raw text vs text + simple features (from the feature‑engineered file).  
- Transfer learning experiments on mental‑health Reddit data.

### Out‑of‑Scope Uses

- Clinical decision‑making, diagnosis, or risk assessment.  
- Any deployment that might influence real‑world medical, therapeutic, or crisis‑intervention workflows.  
- Individual‑level profiling, surveillance, or moderation without appropriate human review and safeguards.

---

## Ethical Considerations and Limitations

- **Non‑clinical labels:** Labels are derived from subreddit context and dataset creators’ heuristics, not formal diagnoses by clinicians.
- **Domain bias:** Content is mostly from Reddit and similar platforms; models may not generalize to clinical notes, private messages, or other domains.  
- **Demographic unknowns:** There is no reliable demographic metadata; potential demographic, cultural, or linguistic biases cannot be quantified.
- **No guarantee of harm‑free samples:** Text may contain distressing content, including mentions of self‑harm or suicide; users should handle and share it with care, especially in teaching settings.

Users are encouraged to:

- Use this dataset **only in controlled research/educational environments**.  
- Avoid building systems that auto‑label individual users in high‑stakes contexts without expert oversight.  
- Follow your institution’s ethics and IRB/IEC guidelines where applicable.

---

## Citation

Please cite the **original datasets** and this derived dataset.

**Original datasets**

- Komati, N. *Suicide and Depression Detection* \[Dataset\]. Kaggle.  
  https://www.kaggle.com/datasets/nikhileswarkomati/suicide-watch

- Sarkar, S. *Sentiment Analysis for Mental Health* \[Dataset\]. Kaggle.  
  https://www.kaggle.com/datasets/suchintikasarkar/sentiment-analysis-for-mental-health

- Murarka, A., Radhakrishnan, B., & Ravichandran, S. (2021). *Detection and Classification of Mental Illnesses on Social Media using RoBERTa* \[Dataset and code\]. GitHub.  
  https://github.com/amurark/mental-health-detection

**This derived dataset**

> Mukherjee, P. (2025). *Mental Health Text Classification Dataset (4‑Class)* \[Dataset\]. Hugging Face Hub. `https://huggingface.co/datasets/ourafla/Mental-Health_Text-Classification_Dataset`.