| --- |
| 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. |
|
|
