I Vibe Coded a Tool to That Analyzes Customer Sentiment and Topics From Call Recordings

2026-04-17 · Source: KDnuggets · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Data Science & Analytics · Depth: Intermediate, medium

Summary

A comprehensive, offline-capable system has been developed to transcribe customer call recordings, analyze sentiment and emotions, and extract recurring topics using open-source AI tools. This project leverages OpenAI's Whisper for audio transcription, Hugging Face Transformers with CardiffNLP's RoBERTa model for sentiment and emotion detection, and BERTopic for automatic topic extraction. The entire solution runs locally, addressing privacy concerns, high API costs, and internet dependency associated with cloud-based AI services. The system integrates these components into an interactive Streamlit dashboard, allowing business users to upload audio files, view real-time processing, and explore results through Plotly visualizations. The setup requires Python 3.9+, FFmpeg, and approximately 2GB of disk space for models.

Key takeaway

For AI Engineers building customer service analytics solutions, this project demonstrates a robust, privacy-preserving architecture. You should consider implementing local, open-source models like Whisper, RoBERTa, and BERTopic to avoid cloud API costs and data residency issues. This approach ensures sensitive customer data remains on-premises while still providing powerful sentiment and topic insights via an interactive Streamlit dashboard.

Key insights

Local, open-source AI tools can process sensitive customer call data for sentiment and topic analysis securely and cost-effectively.

Principles

• Prioritize local processing for sensitive data.
• Modular design simplifies system understanding and extension.
• Choose activation functions based on class independence.

Method

The system transcribes audio with Whisper, analyzes sentiment/emotion using RoBERTa, extracts topics via BERTopic, and visualizes results in a Streamlit dashboard, all running offline.

In practice

• Use Whisper for robust audio transcription.
• Employ RoBERTa for nuanced sentiment analysis.
• Apply BERTopic for unsupervised topic discovery.

Topics

• Customer Call Analytics
• Local AI Processing
• Speech-to-Text Transcription
• Sentiment and Emotion Analysis
• BERTopic Topic Modeling

Code references

• openai/whisper
• zenUnicorn/Customer-Sentiment-analyzer
• cjhutto/vaderSentiment

Best for: AI Engineer, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by KDnuggets.