--- 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 **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`.