Update customer-support-cleaned: cleaned dataset + dataset card

README.md

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+---
+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.

dataset.jsonl

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+{"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}
+{"id": 2, "timestamp": "2024-01-01 10:05:00", "user_message": "我的订单还没有发货", "agent_response": "我们会为您检查物流状态。", "language": "zh", "sentiment": "negative", "message_length": 9}
+{"id": 5, "timestamp": "2024-01-01 10:15:00", "user_message": "Gracias por la ayuda", "agent_response": "De nada.", "language": "es", "sentiment": "positive", "message_length": 20}
+{"id": 6, "timestamp": "2024-01-01 10:20:00", "user_message": "退款申请", "agent_response": "我们已经处理您的退款。", "language": "zh", "sentiment": "negative", "message_length": 4}