Update README.md

README.md

@@ -32,15 +32,15 @@ configs:
 
 ## Dataset Description
 
-This dataset contains short, user‑generated texts labeled for **4‑class mental health classification**: **Suicidal**, **Depression**, **Anxiety**, and **Normal**.[file:64][file:65] 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.[web:24][web:72][web:54][web:58]
+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.).[file:91][file:92]
+- 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.[web:89]
+> **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.
 
 ---
 
@@ -49,20 +49,20 @@ The repository includes:
 ### Files
 
 1. **`mental_heath_unbanlanced.csv`**  
-   - Main training corpus with **48,945 samples** and a realistic, unbalanced class distribution (Normal, Depression, Suicidal, Anxiety).[file:91]  
+   - 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`}.[file:64][file:91]
+     - `status` – Final label in {`Suicidal`, `Depression`, `Anxiety`, `Normal`}.
 
 2. **`mental_health_combined_test.csv`**  
-   - **Balanced test split** with **992 samples** (exactly **248 per class**).[file:91]  
+   - **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.[file:91]
 
 3. **`mental_health_feature_engineered.csv`**  
-   - Cleaned and **feature‑engineered** subset for classical models and analysis.[file:92]  
+   - Cleaned and **feature‑engineered** subset for classical models and analysis. 
    - Columns:
      - Core: `Unique_ID`, `text`, `status` (4‑class label).  
      - Features:
@@ -72,7 +72,7 @@ The repository includes:
        - `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.[file:92]
+       - `avg_word_length` – Average characters per word.
 
 ### Splits
 
@@ -119,18 +119,18 @@ This dataset does **not** collect new data from individuals. Instead, it is buil
 
 - **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[web:24][web:70]
+  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[web:69][web:72]
+  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[web:54][web:58]
+  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.[file:64][file:65]
+Only the **processed, relabeled, and re‑split** samples are redistributed here. Users must obtain the original raw data from the links above if needed.
 
 ---
 
@@ -138,12 +138,12 @@ Only the **processed, relabeled, and re‑split** samples are redistributed here
 
 ### Cleaning
 
-All source datasets are passed through a common cleaning pipeline:[file:91][file:92]
+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.[file:92]
+- Filter out extremely short and excessively long texts to remove non‑informative samples.
 
 ### Unified 4‑Class Labels
 
@@ -152,20 +152,20 @@ Original labels vary widely (suicide vs non, multiple disorders, control). They
 - **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.[web:69][file:64][file:65]
+- **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.[web:69][file:65]
+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.[file:91]  
+- 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.[file:91]
+  - **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.[file:92][web:98]
+The feature‑engineered file is generated from the cleaned sentiment subset by adding the numerical text features listed above.
 
 ---
 
@@ -175,7 +175,7 @@ The feature‑engineered file is generated from the cleaned sentiment subset by
 
 - 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.[web:93][web:100]
+- Studying the impact of **class imbalance**, basic feature engineering, and model robustness on noisy social‑media text.
 
 Typical tasks:
 
@@ -187,16 +187,16 @@ Typical tasks:
 
 - 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.[web:89]
+- 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.[web:24][web:69][web:54]  
-- **Domain bias:** Content is mostly from Reddit and similar platforms; models may not generalize to clinical notes, private messages, or other domains.[web:24][web:69][web:54]  
-- **Demographic unknowns:** There is no reliable demographic metadata; potential demographic, cultural, or linguistic biases cannot be quantified.[web:70]  
-- **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.[web:24][web:69]
+- **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:
 
@@ -213,14 +213,14 @@ 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[web:24]
+  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[web:72]
+  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[web:54][web:58]
+  https://github.com/amurark/mental-health-detection
 
 **This derived dataset**
 
-> Mukherjjee, P. (2025). *Mental Health Text Classification Dataset (4‑Class)* \[Dataset\]. Hugging Face Hub. `https://huggingface.co/datasets/your-username/Mental-Health_Text-Classification_Dataset`.[file:64]
+> Mukherjee, P. (2025). *Mental Health Text Classification Dataset (4‑Class)* \[Dataset\]. Hugging Face Hub. `https://huggingface.co/datasets/your-username/Mental-Health_Text-Classification_Dataset`.