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5М Consumer reviews & Complaints for NLP Training Data
LLM Fine-Tuning Data
Tags and Keywords
£560,000
About
This dataset gives you access to a massive database of real, unedited customer reviews and complaints. Unlike sanitized corporate datasets or synthetic text, this dataset captures the unfiltered reality of human frustration, typos, high-emotion vocabulary, and specific customer demands. It’s perfect 'ground truth' data for data scientists and NLP engineers building language models or text analytics tools.
The uploaded sample file is a preview catalog only and is not the full dataset. Final pricing depends on selected languages, number of audio hours, enrichment files, delivery format, and licensing scope. Average price per record/row is $0,15.
Data Product Features
The dataset clearly separates the customer's story (the review text) from their specific request (the 'wanted solution') into different columns. Key features include:
- Unstructured Verbatim Text: Real human-written descriptions of product failures and service bottlenecks.
- Intent Labels: Features a specific wanted_solution column (e.g., refund, apology), which serves as an ideal label for training intent-classification models.
- User Metadata: Includes device type, precise timestamps, and geographic locations for contextual analysis.
- Anonymized Identity: Uses Hashed Email (HEM - SHA256) as a unique identifier to allow for user journey tracking without exposing personal data.
- PII Redaction: All text fields are strictly processed to remove Personally Identifiable Information (PII) before delivery.
Distribution
- Format: Delivered in CSV or JSON format.
- Data Volume: The full database contains millions of historical complaint records across 140,000+ brands. (This listing acts as a metadata sample).
- Delivery: Custom delivery via secure S3 Bucket, SFTP, or API, customized by date range or specific industry categories.
Usage
This data product is ideal for a variety of AI/ML applications:
- Application: Fine-Tuning LLMs & Chatbots: Exposing models to authentic, emotion-heavy human language to improve automated customer support responses and empathy.
- Application: Sentiment & Emotion Analysis: Training text-classification models to score the severity of consumer frustration and detect legal or escalation threats.
- Application: Intent Classification: Using the wanted_solution field to train algorithms to automatically categorize and route incoming customer tickets.
Coverage
- Geographic Coverage: Global (Includes US, Canada, EU, UK).
- Time Range: 2010 - Present.
- Demographics: B2C consumers interacting with brands across multiple sectors (Airlines, Retail, Finance, E-commerce, etc.).
License
Proprietary
AI Training Rights
Licensee is granted a non-exclusive, worldwide, and perpetual right to:
- Use the Data Product to train, fine-tune, and evaluate machine learning models, including large language models.
- Incorporate Data Product content into models and commercialize resulting model outputs.
- Create derivative works (model weights, embeddings, etc.) for any lawful purpose.
Restrictions:
- The Data Product itself may not be sold, redistributed, or shared outside of licensed usage.
- Licensee must comply with all applicable laws, including data protection and privacy regulations.
Who Can Use It
- Data Scientists & ML Engineers: For training LLMs, sentiment analysis, and intent-classification models.
- AI Startups: Building specialized Customer Experience (CX) or reputation management tools.
- NLP Researchers: For academic or commercial studies on human-computer interaction and emotional language patterns.
Data Dictionary
| Column Name | Data Type | Description | Possible Values/Notes |
|---|---|---|---|
| company_name | String | Name of the public or private company receiving the complaint. | |
| complaint_title | String | The user-generated headline of the complaint. | |
| complaint_text | String | The unstructured, verbatim text detailing the customer's experience. | PII-redacted |
| wanted_solution | String | The specific action or compensation the customer is demanding. | Ideal for Intent labels |
| review_recommendation | String | Advice the complaining user gives to other potential customers. | |
| device_type | String | The platform/device used to submit the complaint. | phoneIos, desktop, etc. |
| activated_date | Datetime | Timestamp when the complaint was published/activated. | YYYY-MM-DD HH:MM:SS |
| country | String | Country where the complaining user is located. | |
| state_province | String | State or province of the user. | |
| city_district | String | City or district of the user. | |
| HEM | String | Hashed Email (SHA-256) serving as an anonymized, unique user identifier. | e.g., b1ca054b7b1f... |
| register_date | Datetime | Timestamp when the user first registered on the platform. | |
| last_visit_date | Datetime | Timestamp of the user's most recent activity on the platform. | |
| catagory_name | String | Industry or sector classification of the company. | e.g., Airlines, Retail |
Note for buyers: For access to larger historical subsets (up to millions of rows) tailored by specific industries, timeframes, or companies, please contact us directly after reviewing the sample schema.
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£560,000
Download Dataset in CSV Format
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