israeli-chatbot-analytics

Hebrew chatbot analytics processes conversation logs to identify drop-offs, loops, and completion rates using Python NLP libraries. It evaluates conversation flows to pinpoint where users disengage or get stuck.

Yes, Hebrew sentiment analysis evaluates user engagement and language mixing in chatbot logs. Using transformers and nltk, it processes Hebrew text to monitor satisfaction and detect confusing prompts.

A/B testing analysis compares response variations in Hebrew chatbot logs to evaluate performance differences. It generates weekly or monthly reports tracking accuracy and user engagement metrics.

Yes, pandas and scikit-learn are required dependencies for processing chatbot logs and generating performance reports. The analytics scripts use these libraries alongside transformers and nltk for comprehensive metrics.

Conversation analytics tracks escalation and abandonment rates by processing Hebrew chatbot interaction logs. It assesses chatbot health by monitoring completion rates and identifying where users abandon conversations.

Language mixing analysis detects when Hebrew chatbot conversations contain mixed languages, impacting user engagement. This feature identifies language inconsistencies that may confuse users and affect sentiment accuracy.