Duplicate from ourafla/Mental-Health_Text-Classification_Dataset

Co-authored-by: Priyangshu Mukherjee <ourafla@users.noreply.huggingface.co>

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+mental_heath_feature_engineered.csv filter=lfs diff=lfs merge=lfs -text
+mental_heath_unbanlanced.csv filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+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`.

mental_health_feature_engineered.csv

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mental_heath_unbanlanced.csv

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