---
license: cc-by-4.0
language:
  - en
  - zh
  - es
tags:
  - customer-support
  - multilingual
  - sentiment-analysis
  - conversational
pretty_name: Customer Support Cleaned
size_categories:
  - n<1K
---

# Customer Support Cleaned

## Dataset Summary

`customer-support-cleaned` is a small, curated multilingual customer-support
conversation dataset derived from a raw Excel workbook (`file1.xlsx`). Each
record contains a customer message (`user_message`), the support agent's reply
(`agent_response`), the conversation language (normalized to ISO 639-1), and a
sentiment label (`positive`, `neutral`, or `negative`).

The dataset is intended for tasks such as:
- Multilingual intent/sentiment classification for customer support,
- Evaluation of response-generation models,
- Demonstrations of data-cleaning and ETL pipelines.

The raw source contains intentionally noisy records (missing values, duplicate
IDs, inconsistent language codes, mixed-case sentiment labels, and duplicated
message pairs). All noise is removed by a reproducible cleaning pipeline (see
[Cleaning Decisions](#cleaning-decisions)), and the resulting artifact is
published as JSON Lines.

## Language Coverage

Language values are normalized to ISO 639-1 codes. Coverage in the released
version:

| Language      | ISO 639-1 code | Count |
|---------------|----------------|-------|
| English       | `en`           | 1     |
| Chinese       | `zh`           | 2     |
| Spanish       | `es`           | 1     |
| **Total**     |                | **4** |

## Data Fields

Each row in `dataset.jsonl` is a JSON object with the following fields (in
order):

| Field             | Type    | Description                                                              |
|-------------------|---------|--------------------------------------------------------------------------|
| `id`              | int     | Unique identifier of the conversation record.                            |
| `timestamp`       | string  | Timestamp of the customer message (`YYYY-MM-DD HH:MM:SS`).               |
| `user_message`    | string  | Customer's message (trimmed).                                            |
| `agent_response`  | string  | Agent's reply (trimmed).                                                 |
| `language`        | string  | ISO 639-1 language code of the conversation (e.g. `en`, `zh`, `es`).     |
| `sentiment`       | string  | Lowercase sentiment label: `positive`, `neutral`, or `negative`.         |
| `message_length`  | int     | Number of characters in `user_message`.                                  |

### Example

```json
{"id": 1, "timestamp": "2024-01-01 10:00:00", "user_message": "How do I reset my password?", "agent_response": "Please click the reset link on the login page.", "language": "en", "sentiment": "neutral", "message_length": 27}
```

## Cleaning Decisions

The raw workbook contained 8 records. The following deterministic pipeline
(`clean_dataset.py`) was applied, in order:

1. **Remove incomplete rows** – Rows where `user_message` or `agent_response`
   is missing (NaN) or blank (empty / whitespace-only) are dropped
   (2 rows removed).
2. **Trim whitespace** – Leading/trailing whitespace is removed from all text
   fields (`timestamp`, `user_message`, `agent_response`, `language`,
   `sentiment`).
3. **Deduplicate message pairs** – Rows with identical `user_message` and
   `agent_response` (after trimming) are dropped, keeping the first occurrence
   (2 rows removed).
4. **Enforce unique IDs** – Any remaining duplicate `id` values are resolved by
   keeping the first occurrence; the number of duplicates removed is recorded
   (0 rows removed after step 3).
5. **Normalize language** – Language values are mapped to ISO 639-1 codes
   (`English` → `en`, `Chinese` → `zh`, `Spanish` → `es`; already-normalized
   codes such as `zh` are kept as-is).
6. **Normalize sentiment** – Sentiment labels are lowercased (`Neutral` →
   `neutral`, `Positive ` → `positive`) and only rows with sentiment in
   {`positive`, `neutral`, `negative`} are retained (labels such as `angry`
   are dropped; 0 rows removed in this step because the `angry` row was
   already removed as incomplete).
7. **Add `message_length`** – An integer column is computed as the character
   count of the trimmed `user_message`.

**Result:** 8 raw records → **4 cleaned records**.

The full cleaning report (counts per step) is printed by `clean_dataset.py` and
is reproduced here for traceability:

```
original_rows: 8
rows_removed_missing_or_blank: 2
rows_removed_duplicate_message_pairs: 2
rows_removed_duplicate_ids: 0
rows_removed_invalid_sentiment: 0
final_rows: 4
```

## Reproducibility

To rebuild the dataset from the raw source:

```bash
pip install pandas openpyxl
python clean_dataset.py file1.xlsx dataset.jsonl
```

## Licensing and Usage Notes

- **License:** This dataset is released under the
  [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) license. You are
  free to share and adapt the material with appropriate attribution.
- **Usage:** Suitable for research and educational purposes, including
  multilingual NLP, sentiment analysis, and customer-support modeling. It is a
  small demo/quality-controlled dataset and should not be treated as a
  representative benchmark for production systems.
- **Privacy:** All messages are synthetic/sample content; no personal or
  identifying information is included.
- **Bias & limitations:** Due to the very small size (4 records), the dataset
  does not claim statistical representativeness. Language and sentiment
  distributions reflect only the cleaned sample.
- **Maintenance:** If the upstream raw data changes, re-run `clean_dataset.py`
  and re-upload `dataset.jsonl` to refresh this repository.