toolathon123/customer-support-cleaned

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), 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

{"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:

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 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.