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tomatosauce-hg/
sentiment-analysis-for-psychological-profiling

Name: sentiment-analysis-for-psychological-profiling
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safetensors

xlm-roberta

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Sentiment Analysis for Psychological Profiling (EN/TL/Taglish)

Model ID: tomatosauce-hg/sentiment-analysis-for-psychological-profiling Base model: xlm-roberta-base (multilingual)

A fine-tuned XLM-R sentence-level sentiment classifier targeted at short free-text responses to psychological questionnaire items. It handles English, Tagalog (Filipino), and Taglish (code-mixed) answers and returns one of three labels:

1 → POS (positive/hopeful/supportive tone)
0 → NEG (negative/distressed/adverse tone)
2 → NEU (neutral/mixed/descriptive tone)

⚠️ Not a clinical tool. This model estimates sentiment only. Do not use it to diagnose or screen for mental health conditions.

Intended Use

Rapid sentiment scoring of open-ended questionnaire responses (e.g., “How do you view yourself?”, “How do you respond to life changes?”).
Works best when you prepend the question to the answer during inference (see “Input formatting”).

How to use

1) Quick pipeline

python

from transformers import pipeline

model_id = "tomatosauce-hg/sentiment-analysis-for-psychological-profiling"

clf = pipeline(
    "text-classification",
    model=model_id,
    tokenizer=model_id,
    truncation=True,
    top_k=None,          # set to None to return only top label
    return_all_scores=False
)

text = "Minsan nahihirapan pero kaya pa naman, sinusubukan kong mag-adjust."
print(clf(text))
# [{'label': 'POS', 'score': 0.83}]  (example)

2) With question + answer (recommended)

python

from transformers import pipeline

model_id = "tomatosauce-hg/sentiment-analysis-for-psychological-profiling"
clf = pipeline("text-classification", model=model_id, tokenizer=model_id, truncation=True)

question = "How do you usually react to life changes? How do you adjust?"
answer   = "Nai-stress ako sa simula, pero inaayos ko routine ko at humihinga muna."
qa_text  = f"[Q] {question}\n[A] {answer}"

print(clf(qa_text))

3) Raw model

python

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_id = "tomatosauce-hg/sentiment-analysis-for-psychological-profiling"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForSequenceClassification.from_pretrained(model_id)

inputs = tok("Okay lang, unti-unti kong tinatanggap at inaayos.", return_tensors="pt", truncation=True)
logits = model(**inputs).logits
pred = torch.argmax(logits, dim=-1).item()
id2label = model.config.id2label  # {'0':'POS','1':'NEG','2':'NEU'}
print(id2label[str(pred)])

Input formatting

The model was trained on answers to specific psychosocial questions. For best results:

If you know the prompt/question, feed [Q] ... [A] ...:

[Q] How do you view the world?
[A] Generally positive; may challenges pero kakayanin.

If you don’t have the question, passing the answer text alone still works.

Max length: 256 WordPiece tokens (XLM-R). Most responses are well under this length.

Training data

Size: ~1,000 synthetic examples (after cleaning/deduping and split: train 733 / val 158 / test 158).
Languages: English, Tagalog, Taglish (code-mixed).
Source: Synthetic generation from a fixed questionnaire set.
Privacy: No real user data. Each sample is synthetic and non-identifiable.

Training details

Base: xlm-roberta-base
Objective: 3-way sentiment classification (POS/NEG/NEU)
Tokenizer: XLM-R SentencePiece
Max length: 256
Batch sizes: train 16, eval 32
Optimizer / LR: AdamW, 2e-5
Epochs: 6 (early stopping enabled)
Warmup: ~6% of total steps
Class weighting: inverse frequency (to stabilize slight skew)
Seed: 42
Hardware: single-GPU/single-machine (modest)

Evaluation (held-out test set, n=158)

Accuracy: 0.82
Macro-F1: 0.82

Per-class (precision / recall / F1):

Label	P	R	F1	Support
POS (1)	0.90	0.85	0.88	55
NEG (0)	0.79	0.88	0.83	51
NEU (2)	0.78	0.73	0.75	52

Observations

NEU is the hardest class (common in sentiment tasks).
POS/NEG are strong and balanced.
Performance is solid for a first pass on synthetic data; expect gains after fine-tuning on small amounts of real data from your domain.

Limitations & risks

Synthetic domain gap: Real client language may differ; expect some drift.
Not a diagnostic tool: This model measures tone, not mental health status.
Code-mixing edge cases: Extremely slangy or heavily code-mixed text may reduce confidence.
Question dependence: Including the question often improves robustness.

Reproducing

This repo includes:

config.json, model.safetensors, tokenizer files
You can fine-tune further with standard Hugging Face Trainer on your dataset (same id2label/label2id mapping).

Label mapping

json

"id2label": {"1": "POS", "0": "NEG", "2": "NEU"},
"label2id": {"POS": 1, "NEG": 0, "NEU": 2}

Example batch inference

python

from transformers import pipeline

model_id = "tomatosauce-hg/sentiment-analysis-for-psychological-profiling"
clf = pipeline("text-classification", model=model_id, tokenizer=model_id, truncation=True)

batch = [
    "Okay naman ako physically; minsan pagod lang.",
    "Di ko gusto ang nangyayari, sobrang nakakapagod na.",
    "Neutral lang—may good at bad, pero tuloy lang."
]
print([x['label'] for x in clf(batch)])

Citation

If you use this model, please cite:

@software{tomatosauce_hg_sentiment_psych_2025,
  title  = {Sentiment Analysis for Psychological Profiling (EN/TL/Taglish)},
  author = {tomatosauce-hg},
  year   = {2025},
  url    = {https://huggingface.co/tomatosauce-hg/sentiment-analysis-for-psychological-profiling}
}

License

Base model: xlm-roberta-base (MIT).
Fine-tuned weights: Apache-2.0

Contact / Issues

Open an issue on the model repo if you hit problems or have improvement ideas (e.g., real-data fine-tuning, calibration, question-aware prompts).