---
license: mit
task_categories:
- text-classification
language:
- en
tags:
- classification,
- sentiment-analysis,
- binary-classification,
- complex-text
- jsonl
size_categories:
- 100K<n<1M
---
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:

```json
{
  "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

| Field   | Type    | Description                              |
| ------- | ------- | ---------------------------------------- |
| `id`    | Integer | Unique sample identifier                 |
| `text`  | String  | Input sentence (20–40 tokens)            |
| `label` | String  | Sentiment class (`positive`, `negative`) |

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

```python
from datasets import load_dataset

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

Streaming mode:

```python
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.

---