NNEngine/Sentiment-Analysis-Complex

Excellent — congrats on getting the repo ready 🚀 Here’s a professional Hugging Face Dataset Card (README.md) you can paste directly into your repository.

This is written to match HF best practices and serious research usage.

📘 README.md

👉 Copy everything below into your README.md

Sentiment-Analysis-Complex

🧠 Overview

Sentiment-Analysis-Complex is a large-scale synthetic sentiment analysis dataset designed for benchmarking modern NLP models under long-context, noisy, and semi-structured text conditions.

The dataset contains 10 million labeled samples with:

• —Long text sequences (20–40 tokens per sample)
• —Grammar-based sentence construction
• —Internet slang and hashtags
• —Rich vocabulary diversity
• —Balanced binary sentiment labels

It is optimized for:

• —Transformer benchmarking
• —Tokenizer stress testing
• —Long-context modeling
• —Robustness evaluation
• —Large-scale NLP pipelines

📦 Dataset Structure

Sentiment-Analysis-Complex/
 ├── train.jsonl   (8,000,000 samples)
 ├── test.jsonl    (2,000,000 samples)
 └── README.md

Split ratio:

• —Train: 80%
• —Test: 20%

🧾 Data Format

Each line is a JSON object:

{
  "id": 123456,
  "text": "I really love how this system consistently delivers smooth reliable performance and scalable architecture with intuitive workflow and strong documentation lol #innovation",
  "label": "positive"
}

Fields

Encoding: UTF-8 (emoji and special characters supported)

📊 Dataset Characteristics

• —✔️ Total samples: 10,000,000
• —✔️ Classes: positive / negative (balanced)
• —✔️ Sequence length: 20–40 tokens
• —✔️ Vocabulary size: ~300+ words
• —✔️ Includes slang and hashtags
• —✔️ Grammar-driven generation
• —✔️ Streaming-friendly JSONL format

🔬 Intended Use

This dataset is suitable for:

• —Sentiment classification benchmarking
• —Large-scale training pipelines
• —Tokenization analysis
• —Long-context modeling experiments
• —Data loading stress tests
• —Distributed training validation
• —Synthetic NLP research

⚠️ Limitations

• —Synthetic text — not reflective of natural human distribution.
• —Limited semantic depth and discourse structure.
• —No real-world bias modeling.
• —No multilingual coverage (English only).
• —No sarcasm or pragmatic reasoning.

Not recommended for production sentiment systems.

🤗 How to Load

from datasets import load_dataset

dataset = load_dataset("NNEngine/Sentiment-Analysis-Complex")
print(dataset)

Streaming mode:

dataset = load_dataset(
    "NNEngine/Sentiment-Analysis-Complex",
    streaming=True
)

🏷️ Tags

sentiment-analysis
nlp
synthetic-data
large-scale
text-classification
benchmark
huggingface-dataset
long-context

📜 License

MIT License Free for research, education, and experimentation.

✨ Author

Created by NNEngine for large-scale NLP benchmarking and experimentation.