tahamueed23/english-sentiment-roberta-large

🇬🇧 English Sentiment Analysis — Fine-tuned RoBERTa-Large

A Fine-tuned RoBERTa-Large Model for 3-Class English Sentiment Classification

Trained on educational and consumer feedback data for high-precision sentiment understanding

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1. Model Overview

This model is a fine-tuned RoBERTa-Large classifier for English sentiment analysis, adapted from siebert/sentiment-roberta-large-english. It classifies English text into three sentiment categories — Positive, Neutral, and Negative — with high confidence and strong generalization across diverse feedback domains.

The model was trained on a curated dataset of English-language educational and consumer feedback, and achieves 92.12% accuracy on the held-out test set, making it well-suited for production-grade NLP pipelines.

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2. Model Details

Attribute	Value
Base Model	siebert/sentiment-roberta-large-english
Architecture	RoBERTa-Large (Transformer Encoder)
Task	3-class Sentiment Classification
Classes	Negative (0), Neutral (1), Positive (2)
Total Parameters	355,362,819 (~355M)
Model Size	~1.42 GB
Max Sequence Length	256 tokens
Training Framework	Hugging Face Transformers + PyTorch
Compute	Mixed Precision (FP16)
Repository	tahamueed23/english-sentiment-roberta-large
Last Updated	February 22, 2026
Author	Taha Mueed

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3. Intended Use

This model is designed for any downstream task that requires classifying the sentiment of English text into three categories: Positive, Neutral, or Negative.

Domain	Application
🎓 Education	Student feedback analysis, course evaluation, teacher reviews
🛒 E-commerce	Product review classification, customer satisfaction scoring
📱 Social Media	Comment moderation, brand monitoring, opinion mining
🏢 Customer Support	Ticket sentiment tagging, call centre transcript analysis
🏥 Healthcare	Patient experience reviews, hospital feedback analysis
📊 Market Research	Consumer opinion tracking, NPS survey classification
🛡️ Content Moderation	Detecting hostile or negative user-generated content

Out-of-Scope Use

This model is designed for English-only input. It is not suitable for multilingual text, code-switched content, or non-English languages. It was also not trained for tasks like emotion detection beyond sentiment polarity, toxicity classification, or stance detection.

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4. Dataset Description

The training data was sourced from a multilingual academic feedback dataset hosted on Google Sheets, containing 50,056 records across five languages. Only English-language entries were selected for this model's training.

Full Dataset Composition (N = 50,056)

Language	Count	Percentage
Roman Urdu	16,916	33.79%
Urdu	16,782	33.52%
English	16,355	32.67%
Mixed / Other	3	0.01%

English Subset — After Filtering and Cleaning (N = 15,650)

Sentiment Class	Count	Percentage
Positive	7,160	45.75%
Negative	5,364	34.27%
Neutral	3,126	19.97%

The dataset exhibits a moderate class imbalance, with Neutral being the least represented class. This was addressed through class-weighted loss during training (see Section 7).

Train / Validation / Test Split

Split	Samples	Percentage
Training	10,955	70.0%
Validation	2,347	15.0%
Test	2,348	15.0%
Total	15,650	100%

Splits were created using stratified sampling to preserve the original class distribution across all three subsets.

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5. Data Preprocessing

The following cleaning and normalization steps were applied to the raw English text before training:

1. Duplicate removal — 701 duplicate entries were removed based on exact match of the feedback field.
2. Null filtering — Records with missing feedback or senti_manual values were dropped.
3. Whitespace normalization — Consecutive whitespace characters were collapsed using regex substitution.
4. Short text removal — Texts shorter than 5 characters were discarded as uninformative.
5. Empty string removal — Any remaining blank entries after cleaning were excluded.
6. Label standardization — Sentiment labels were title-cased and mapped to integers: Negative → 0, Neutral → 1, Positive → 2.
7. Label validation — Rows with labels outside the valid set {0, 1, 2} were dropped.

After preprocessing, the final cleaned dataset contained 15,650 samples, reduced from 16,355 raw English entries.

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6. Training Procedure

The model was initialized from siebert/sentiment-roberta-large-english (originally a binary classifier) and adapted to a 3-class classification head by re-initializing the output projection layer (classifier.out_proj) to output 3 logits instead of 2.

Training used a custom weighted loss Trainer (WeightedLossTrainer) that applies per-class weights during cross-entropy loss computation to compensate for the Neutral class underrepresentation.

Class Weights Applied During Training

Class	Weight
Negative	0.9725
Neutral	1.6688
Positive	0.7286

Weights were computed using sklearn.utils.class_weight.compute_class_weight with the balanced strategy.

Additional training strategies included gradient checkpointing to reduce GPU memory usage, FP16 mixed precision for faster computation, and early stopping with a patience of 3 evaluation cycles to prevent overfitting. Model checkpoints were evaluated every 100 steps on the validation set using weighted F1 as the selection criterion, and the best checkpoint was loaded at the end of training.

Total training time: 4,332.72 seconds (~72 minutes) on an NVIDIA Tesla T4 (15.64 GB VRAM).

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7. Hyperparameters

Parameter	Value
Epochs	4
Per-device Train Batch Size	8
Per-device Eval Batch Size	16
Gradient Accumulation Steps	2 (effective batch size = 16)
Learning Rate	2e-5
LR Scheduler	Cosine
Warmup Steps	500
Weight Decay	0.01
Optimizer	AdamW (adamw_torch)
Mixed Precision	FP16
Gradient Checkpointing	Yes
Max Sequence Length	256 tokens
Early Stopping Patience	3 evaluation cycles
Eval / Save Strategy	Every 100 steps
Best Model Selection Metric	Weighted F1
Random Seed	42

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8. Evaluation Metrics

All metrics reported are computed on the held-out test set (2,348 samples) unless stated otherwise.

Overall Performance

Metric	Score
Accuracy	92.12%
F1-Score (Weighted)	91.91%
Precision (Weighted)	91.98%
Recall (Weighted)	92.12%

Validation Set Performance

Metric	Score
Validation Loss	0.4273
Accuracy	92.54%
F1-Score (Weighted)	92.42%
Precision (Weighted)	92.46%
Recall (Weighted)	92.54%

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9. Results

Per-Class Performance (Test Set)

Class	Precision	Recall	F1-Score	Support
Negative	91.73%	96.40%	94.00%	805
Neutral	87.94%	74.63%	80.74%	469
Positive	93.93%	96.55%	95.22%	1,074
Weighted Avg	91.98%	92.12%	91.91%	2,348

Confusion Matrix

	Predicted: Negative	Predicted: Neutral	Predicted: Positive
Actual: Negative	776	22	7
Actual: Neutral	59	350	60
Actual: Positive	11	26	1,037

The model performs exceptionally well on the Positive and Negative classes, with F1 scores above 94%. The Neutral class, as expected given its lower training representation and linguistic ambiguity, shows a slightly lower recall (74.63%), indicating that some neutral texts are misclassified as positive — a common challenge in 3-class sentiment tasks.

Error Rate: 185 misclassifications out of 2,348 samples (7.88%)

Sample Predictions

Input Text	Predicted Sentiment	Confidence
"This product is absolutely amazing! Best purchase ever!"	Positive 🟢	99.88%
"Terrible quality and poor customer service."	Negative 🔴	99.64%
"It's okay, nothing special but does the job."	Neutral 🟡	99.49%
"The outdoor seating areas are pleasant and encourage socializing."	Positive 🟢	99.90%
"Very disappointed with this purchase."	Negative 🔴	99.63%
"Average service, could be better but could be worse."	Neutral 🟡	99.68%
"Exceptional quality and fantastic customer support!"	Positive 🟢	99.90%

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💻 How to Use

Method 1: Pipeline API (Quickest)

from transformers import pipeline

# Load the model
classifier = pipeline(
    "sentiment-analysis",
    model="tahamueed23/english-sentiment-roberta-large"
)

# Single prediction
result = classifier("This is an amazing product!")[0]

# Map label to sentiment
sentiment_map = {'LABEL_2': 'Positive', 'LABEL_1': 'Neutral', 'LABEL_0': 'Negative'}
print(f"Sentiment:  {sentiment_map[result['label']]}")
print(f"Confidence: {result['score']:.2%}")

Method 2: Manual Inference

from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch

model_name = "tahamueed23/english-sentiment-roberta-large"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

text = "The lecture was well-structured and easy to follow."
inputs = tokenizer(text, return_tensors="pt", padding=True,
                   truncation=True, max_length=256)

model.eval()
with torch.no_grad():
    outputs = model(**inputs)
    probabilities = torch.softmax(outputs.logits, dim=-1)
    predicted_class = torch.argmax(probabilities, dim=1).item()
    confidence = probabilities[0][predicted_class].item()

sentiment_map = {0: "Negative", 1: "Neutral", 2: "Positive"}
print(f"Text:       {text}")
print(f"Sentiment:  {sentiment_map[predicted_class]}")
print(f"Confidence: {confidence:.2%}")

# All class probabilities
for i, label in sentiment_map.items():
    print(f"  {label}: {probabilities[0][i]:.2%}")

Method 3: Batch Processing

from transformers import pipeline

classifier = pipeline(
    "sentiment-analysis",
    model="tahamueed23/english-sentiment-roberta-large"
)

texts = [
    "Best experience I've had with this service!",
    "Very disappointing — would not recommend.",
    "It's acceptable, nothing remarkable.",
    "The support team resolved my issue quickly.",
    "The product broke after two days."
]

results = classifier(texts, batch_size=16)

sentiment_map = {'LABEL_2': 'Positive', 'LABEL_1': 'Neutral', 'LABEL_0': 'Negative'}
for text, result in zip(texts, results):
    sentiment = sentiment_map[result['label']]
    print(f"  {text[:60]:<60} → {sentiment:<8} ({result['score']:.2%})")

Method 4: Hugging Face Inference API

import requests

API_URL = "https://api-inference.huggingface.co/models/tahamueed23/english-sentiment-roberta-large"
headers = {"Authorization": "Bearer YOUR_HF_TOKEN"}

def query(text):
    response = requests.post(API_URL, headers=headers, json={"inputs": text})
    return response.json()

output = query("The course material was very well organized.")
print(output)

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10. Limitations

Language and Input Constraints

Limitation	Severity	Notes
Non-English text	❌ High	Model is designed exclusively for English input
Very short texts (< 5 chars)	❌ High	Insufficient context for reliable classification
Code-switched or mixed-language text	⚠️ Medium	May produce unreliable predictions
Sarcasm and irony	⚠️ Medium	Nuanced text may be misclassified
Domain-specific jargon	⚠️ Low	Performance may degrade on highly specialized content
Emojis only (no text)	❌ High	Requires at least some textual content

Technical Constraints

Constraint	Value	Notes
Max Sequence Length	256 tokens	Longer texts will be truncated
Model Size	~1.42 GB	Quantization recommended for low-memory environments
CPU Inference Speed	~450ms/sample	GPU strongly recommended for production use
GPU Inference Speed	~45ms/sample	Real-time capable on modern GPUs

Known Weaknesses

The model shows its weakest performance on the Neutral class (F1: 80.74%, Recall: 74.63%), which is a known challenge in 3-class sentiment tasks. Texts that contain both positive and negative elements, or those that are factually descriptive without strong polarity, are most likely to be misclassified. Error analysis reveals that the most common mistake is misclassifying neutral educational feedback as positive, particularly when the text contains surface-level positive language without an explicit sentiment intent.

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11. Ethical Considerations

This model was trained on feedback collected from educational and consumer contexts. Users should be aware of the following considerations before deploying it:

Bias. The training data reflects the language patterns and sentiments present in specific feedback domains (primarily academic and product reviews). The model may not generalize equitably across all demographic groups, writing styles, or cultural contexts. It could underperform on dialects or informal registers underrepresented in the training corpus.

Responsible deployment. Sentiment predictions should not be used as the sole basis for high-stakes decisions affecting individuals (e.g., employee performance review, student grading, or content banning). Human review is strongly recommended for sensitive applications.

Data privacy. Ensure that any text submitted for inference does not contain personally identifiable information (PII), as text is processed through the model at inference time.

Feedback misinterpretation. The Neutral class has lower recall, meaning some genuinely neutral or ambiguous feedback may be incorrectly flagged as Positive or Negative. Downstream consumers should be designed to tolerate this level of uncertainty.

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12. Future Improvements

Planned Update	Version	Description
Quantized Version	v1.0.1	INT8 quantization for ~75% model size reduction
Improved Neutral Detection	v1.1.0	Data augmentation and focal loss to address class imbalance
Domain Adaptation	v1.2.0	Specialized fine-tuning for healthcare, legal, or financial text
4-Class Emotion Detection	v2.0.0	Extending to: Joy, Anger, Sadness, Neutral
Multilingual Support	v2.1.0	Cross-lingual model supporting English + Urdu + Roman Urdu
Gradio Demo	—	Interactive web demo on Hugging Face Spaces

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📚 Citation

If you use this model in your research or project, please cite:

@misc{english_sentiment_roberta_2026,
  author    = {Taha Mueed},
  title     = {English Sentiment Analysis: A Fine-tuned RoBERTa-Large Model for 3-Class Sentiment Classification},
  year      = {2026},
  publisher = {Hugging Face Hub},
  howpublished = {\url{https://huggingface.co/tahamueed23/english-sentiment-roberta-large}},
  note      = {Version 1.0, Test Accuracy: 92.12\%, F1: 91.91\%}
}

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📄 License

This model is released under the MIT License.

MIT License

Copyright (c) 2026 Taha Mueed

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.

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📬 Contact & Support

Channel	Details	Response Time
🤗 Hugging Face	tahamueed23/english-sentiment-roberta-large	< 24h
📧 Email	tahamueed23@gmail.com	< 72h

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🙏 Acknowledgments

• Hugging Face — Transformers library and model hosting infrastructure
• siebert / Cardiff NLP — Base RoBERTa-Large sentiment model and research
• PyTorch Team — Deep learning framework
• scikit-learn — Evaluation metrics and class weight computation

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Built for reliable, high-precision English sentiment understanding

_{© 2026 Taha Mueed • MIT License • Version 1.0.0}