---
language:
- en
license: other
task_categories:
- text-classification
- conversational
task_ids:
- intent-classification
- sentiment-classification
tags:
- ecommerce
- customer-service
- synthetic
- intent
- sentiment
- chatbot
- dialogue
- jsonl
size_categories:
- 1K<n<10K
---

# EcommerceAI Dataset — English E-commerce Customer Service

## Dataset Description

A high-quality **synthetic** English-language dataset of e-commerce 
customer service conversations with full intent and sentiment annotations.

> ⚠️ **Transparency Notice:** This is a synthetic dataset. All conversations 
> are programmatically generated. No real customer data is included. 
> Every record is labeled `"data_type": "SYNTHETIC"` in metadata.

---

## Dataset Stats

| Metric | Value |
|--------|-------|
| Conversations | 5,000 |
| Dialogue Turns | 47,028 |
| Avg Turns/Conversation | 9.4 |
| Language | English |
| Issue Categories | 5 |
| Product Categories | 10 |
| Intent Classes | 12 |

---

## Issue Categories

| Category | Count |
|----------|-------|
| Late Delivery | 1,000 |
| Wrong Item Received | 1,000 |
| Refund Request | 1,000 |
| Damaged Item | 1,000 |
| Product Inquiry | 1,000 |

---

## Data Schema

```json
{
  "id": "uuid-v4",
  "metadata": {
    "domain": "ecommerce_customer_service",
    "issue_type": "late_delivery",
    "product_category": "electronics",
    "language": "en",
    "data_type": "SYNTHETIC",
    "quality_tier": "enterprise",
    "turns_count": 10
  },
  "conversation": [
    {
      "role": "user",
      "content": "My order hasn't arrived in 7 days.",
      "intent": "late_delivery",
      "sentiment": "negative"
    },
    {
      "role": "agent",
      "content": "I'm sorry to hear that. Let me check your order.",
      "intent": "acknowledge",
      "sentiment": "positive"
    }
  ]
}
```

---

## Quick Load

```python
# Option 1 — HuggingFace Datasets
from datasets import load_dataset
ds = load_dataset("YOUR_USERNAME/ecommerce-cs-dataset")

# Option 2 — Pure Python
import json
conversations = []
with open('ecommerce_cs_en_synthetic.jsonl') as f:
    for line in f:
        conversations.append(json.loads(line))

# Filter by issue type
refunds = [c for c in conversations 
           if c['metadata']['issue_type'] == 'refund_request']
```

---

## Use Cases

- ✅ Fine-tuning LLMs for customer service chatbots
- ✅ Training intent classifiers (12 classes)
- ✅ Training sentiment analysis models
- ✅ Dialogue state tracking research
- ✅ Augmenting real-world datasets
- ✅ Testing chatbot pipelines

---

## 🔒 Full Dataset (Commercial License)

This repository contains a **free sample of 500 conversations.**

The full dataset **(5,000 conversations, 47K+ turns)** with commercial 
license is available here:

👉 **[Get Full Dataset on Sellix → YOUR_SELLIX_LINK]**

Includes:
- Full 5,000 conversation JSONL
- Complete JSON array format
- Data card & documentation
- Commercial use license

---

## License

Sample (this repo): CC BY-NC 4.0 — free for research & personal use.  
Full dataset: Commercial license — see Sellix listing for terms.

---

## Citation

If you use this dataset in research, please cite: