Tech Support Conversations Dataset
Tech Support Conversations Dataset
Datasets
Tech Support Conversations Dataset
File
Tech Support Conversations
Use Case
Tech Support Conversations
Description
The Tech Support Conversations Dataset containing 1900+ synthetic interactions between customers and support agents. Ideal for training AI models in sentiment analysis
Description:
The Tech Support Conversations Dataset provides a robust collection of synthetic, real-world-inspired data designed to mimic actual interactions between customers and tech support agents. This dataset is curated to cover a diverse range of customer issues that tech support teams frequently encounter. It includes a variety of technical problems, such as:
- Network Connectivity Issues: Problems with internet connectivity, Wi-Fi setup, VPN configurations, and network troubleshooting.
- Account-Related Queries: Account login issues, password resets, user permissions, and account recovery.
- Hardware Failures: Device malfunctions, hardware breakdowns, component failures, and troubleshooting of physical tech devices.
- Software Errors and Bugs: Application crashes, software installation failures, compatibility issues, and error troubleshooting.
- System Performance Issues: Slow computer performance, system freezes, memory overload, and system optimization.
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Each entry in the dataset is structured with:
- A unique conversation ID
- Customer issue description
- Support agent response
- Time taken to resolve the issue
- Issue category (e.g., Network, Account, Hardware, Software)
- Resolution status (e.g., “Resolved”, “Pending”, “Escalated”)
With approximately 1900 rows of data, each row represents an individual tech support conversation, providing a comprehensive set of scenarios for analysis. This dataset is ideal for a variety of machine learning applications like:
- Sentiment analysis to evaluate customer emotions
- Issue classification to categorize tech support queries
- Resolution time prediction for optimizing support workflows
The Dataset is perfect for training AI models that aim to improve customer support automation, predictive analytics, and sentiment analysis.
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