FineGrainedDepressionEmo is a sentence-level emotion classification dataset derived from long-form, depression-related user posts. Each post is split into sentences and each sentence is annotated with a fine-grained emotion label capturing distinct facets of depressive experience (e.g., sadness, hopelessness, worthlessness, suicide intent).

The dataset is designed for studying fine-grained emotional signals in mental health narratives and for evaluating context-aware models that operate over sentence sequences within a thread.

Supported Tasks and Leaderboards

Primary task: single-label multi-class emotion classification at sentence level.

Given a sentence, the goal is to predict one of the following emotion labels:

anger
brain dysfunction
emptiness
hopelessness
loneliness
sadness
suicide intent
worthlessness

There is no official leaderboard yet, but the dataset is suitable for:

Baseline sentence classification with BERT-like models (C0, no context).
Context-aware models that include previous/next sentences or full threads (C1–C3 in the accompanying EmoShiftNet code).

Languages

English (informal, user-generated, often Reddit-style narratives).

Dataset Structure

Data Instances

Each row corresponds to a single sentence from a depression-related narrative.

Example (HF-sanitized version):

item_id,sentence,emotion_final
hhcq6e_1,My mum had a boyfriend when I was around 6 or 7.,sadness
hhcq6e_2,She met him while she was volunteering at a prison.,sadness

Data Fields

In the Hugging Face upload variant, each CSV file has exactly three columns:

item_id (string): identifier for the sentence. Sentences from the same original post share a common base id, and in the HF-ready copy each sentence is made unique by appending a 1-based index within that thread (e.g., hhcq6e_1, hhcq6e_2, …). The numeric suffix reflects sentence order in the original narrative.
sentence (string): the sentence text.
emotion_final (string): the final human-validated emotion label, one of: anger, brain dysfunction, emptiness, hopelessness, loneliness, sadness, suicide intent, worthlessness.

Data Splits

Typical files:

train_merged_final_emotions_with_final_label.csv
val_merged_final_emotions_with_final_label.csv
test_merged_final_emotions_with_final_label.csv
all_merged_final_emotions_with_final_label.csv

Split sizes depend on the exact configuration, but the combined file (all_merged_final_emotions_with_final_label.csv) contains 32,347 labeled sentences.

Label Distribution

Label counts in the full combined file:

sadness: 10,023
hopelessness: 6,494
loneliness: 5,107
anger: 4,168
worthlessness: 2,250
suicide intent: 1,821
emptiness: 1,720
brain dysfunction: 764

The label distribution is moderately imbalanced; macro-averaged metrics (macro F1) are recommended when reporting results.

Text Characteristics

Sentences are mostly well-formed English, often written in first person and describing subjective, depression-related experiences.

End-of-sentence punctuation in the combined file:

. (period) — 27,507 sentences
? (question mark) — 2,260 sentences
! (exclamation mark) — 425 sentences

All other final characters (letters, closing brackets, quotes, ellipsis, etc.) are comparatively rare. This is helpful when choosing maximum sequence length and when designing sentence-boundary-aware models.

Data Processing and Annotations

High-level pipeline (see the EmoShiftNet repository for full details):

Collect long-form, depression-related posts.
Split posts into sentences.
Derive a final consensus / manually validated label emotion_final.
For the Hugging Face upload:
- Remove intermediate model-based label columns.
- Make item_id sentence-unique via a per-thread index suffix.

Usage

In Python with datasets:

from datasets import load_dataset

ds = load_dataset("samanjoy2/FineGrainedDepressionEmo")
df = ds["train"].to_pandas()  # if you define train/val/test splits

Typical modeling steps:

Use sentence as input text.
Encode emotion_final as class labels.
Optionally recover base thread ids by stripping the numeric suffix from item_id (e.g., hhcq6e_1 → hhcq6e) and group sentences to build context-aware inputs (previous/next sentences, full thread, etc.).

Ethical Considerations

The dataset originates from user-generated content describing mental health and depression-related experiences.

Users should:

Avoid attempts to identify or deanonymize individuals.
Avoid deploying models trained on this data in high-stakes clinical settings without appropriate oversight.
Clearly communicate limitations and potential biases of any models trained on this dataset.

Citation

If you use this dataset in academic work, please cite the associated EmoShiftNet paper or repository (add citation details here when available).

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