Customer Service Chatbot Dialogues - 5000 Labeled Conversations with I

A comprehensive collection of 5,000 labeled customer service chatbot dialogues designed for training conversational AI systems, intent classification models, and sentiment analysis algorithms. This dataset captures realistic customer-bot interactions across multiple service scenarios including delivery inquiries, account management, payment issues, login support, and product setup assistance. Each conversation pair is annotated with intent labels and sentiment scores, making it production-ready for supervised machine learning applications in customer service automation, virtual assistant development, and natural language understanding research.

Customer query or statement representing real-world customer service interactions. Contains synthetic text covering common scenarios such as delivery tracking, billing disputes, account issues, password resets, refund requests, technical troubleshooting, and general product inquiries. Messages vary in complexity and tone to reflect authentic customer communication patterns.

Chatbot response tailored to address the user’s query. Contains helpful, contextually appropriate responses demonstrating professional customer service language, problem-solving approaches, and conversational tone suitable for automated support systems. Categorical label identifying the primary purpose or goal of the user’s message. Includes 10 distinct intent categories: refund_request, complaint, billing_issue, greeting, goodbye, order_status, tech_support, account_inquiry, login_assistance, and setup_help. Essential for training intent classification models and routing systems. 4. sentiment: Emotional tone label indicating the sentiment expressed in the user message. Three-class classification: positive (satisfied, friendly), neutral (factual, informational), and negative (frustrated, dissatisfied). Critical for sentiment analysis training and customer experience monitoring systems.

: Single CSV file with UTF-8 encoding, comma-separated values with header row. Clean structure with no missing values or malformed entries. Adatmennyiség: • Total records: 5,000 conversation pairs • Columns: 4 (user_message, bot_reply, intent, sentiment) • File size: ~650 KB • Format: CSV (Comma-Separated Values) Szerkezet: Tabular format with one conversation exchange per row. Each record contains a complete user-bot interaction with corresponding intent and sentiment annotations. Balanced distribution across intent categories and sentiment classes to prevent model bias. Ready for immediate import into popular ML frameworks (scikit-learn, TensorFlow, PyTorch, Hugging Face Transformers). Label Distribution: • Intent classes: 10 categories with approximately balanced representation • Sentiment classes: 3 categories (positive, neutral, negative) distributed across realistic customer service scenarios • Topics: Delivery, account, payment, login, setup covering 90%+ of common customer service interactions

Ez az adathalmaz ideális számos alkalmazáshoz: Alkalmazás: Intent Classification Training - Build and train NLP models to automatically identify customer intent, enabling intelligent routing of support tickets, chatbot response selection, and automated workflow triggers in customer service platforms. Alkalmazás: Sentiment Analysis Development - Train sentiment detection models to monitor customer satisfaction in real-time, flag negative interactions for human escalation, and measure service quality metrics across support channels. Alkalmazás: Chatbot and Virtual Assistant Training - Fine-tune conversational AI models (BERT, GPT, RASA) for customer service applications, developing context-aware response generation and natural dialogue flow capabilities. Alkalmazás: Customer Service Automation - Build end-to-end automated support systems that understand user requests, detect emotional state, and provide appropriate responses without human intervention, reducing support costs by 40-60%. Alkalmazás: Multi-Task Learning Research - Leverage dual annotations (intent + sentiment) for advanced NLP research on joint prediction tasks, transfer learning experiments, and multi-label classification architectures. Alkalmazás: Conversational AI Testing - Benchmark and evaluate existing chatbot systems against standardized conversation scenarios, measure intent accuracy, sentiment detection performance, and response quality metrics. Alkalmazás: Training Data Augmentation - Use as seed dataset for generating additional synthetic conversations through paraphrasing, back-translation, or generative AI techniques to create larger custom training sets.

Földrajzi lefedettség: Global - language-neutral conversation patterns applicable to international customer service operations. English language dataset suitable for worldwide e-commerce, SaaS, telecommunications, banking, and retail industries. Időtartomány: Dataset created December 2025, reflecting contemporary customer service language patterns, common digital service issues, and modern conversational AI standards. Demográfiai adatok: Represents diverse customer profiles across age groups, technical proficiency levels, and communication styles. Covers B2C (business-to-consumer) and B2B (business-to-business) service interactions. Applicable to industries including e-commerce, fintech, telecommunications, SaaS platforms, online marketplaces, digital banking, and subscription services. Domain Coverage: • E-commerce customer support (45%) • Technical support and troubleshooting (25%) • Account and billing management (20%) • General inquiries and navigation (10%)

Proprietary

Adattudósok: Train production-grade intent classification and sentiment analysis models using scikit-learn, TensorFlow, or PyTorch. Develop multi-task learning architectures and evaluate model performance with pre-labeled ground truth data. Kutatók: Conduct academic research on conversational AI, natural language understanding, emotion detection in text, and human-computer interaction. Publish benchmarks and comparative studies using standardized labeled dataset. Vállalkozások: Deploy customer service automation solutions, reduce support costs, improve response times, and scale support operations without proportional headcount increases. Build proprietary chatbots customized to brand voice and service policies. Fejlesztők: Integrate pre-trained models into applications, test chatbot prototypes, validate intent detection accuracy, and demonstrate AI capabilities to stakeholders using realistic customer service scenarios. AI/ML mérnökök: Fine-tune large language models (LLMs) for domain-specific customer service applications, implement RASA or Dialogflow-based systems, and create intelligent routing mechanisms based on intent classification. Startupok: Rapidly prototype conversational AI MVPs, demonstrate chatbot capabilities to investors, and build initial customer support automation without expensive data collection and annotation processes. Oktatók: Teach NLP, machine learning, and conversational AI courses with practical hands-on exercises using real-world dataset structure and industry-relevant use cases.

✅ Dual-Label Advantage: Unique combination of intent and sentiment annotations enables sophisticated multi-dimensional analysis and advanced model architectures that outperform single-task systems. ✅ Production-Ready Quality: Clean, validated data with consistent labeling standards, no missing values, proper formatting, and balanced class distribution reduces data preprocessing time by 70%. ✅ Synthetic Data Benefits: Zero privacy concerns, GDPR/CCPA compliant, ethically sourced, eliminates customer consent requirements, and safe for public sharing, testing, and commercial deployment. ✅ Framework Compatible: Works seamlessly with popular ML libraries including Hugging Face Transformers, scikit-learn, spaCy, TensorFlow, PyTorch, RASA, Dialogflow, and custom neural network architectures. ✅ Scalable Foundation: Use as training base and augment with domain-specific examples, customer feedback, or generative AI to create 10K, 50K, or 100K+ record datasets tailored to specific business needs. ✅ Immediate ROI: Deploy trained models within 2-4 weeks, achieving 60-80% automation rates in tier-1 customer support, reducing average response time from hours to seconds, and improving customer satisfaction scores. ✅ Regular Updates Available: Contact for expanded versions with additional intents, multilingual support, multi-turn conversations, and industry-specific customizations.