Build an AI Tool to Analyze Customer Sentiment from Call Recordings
This article provides a step-by-step guide to build an AI-powered customer sentiment analysis tool for call recordings using open-source tools like Whisper, BERTopic, and Streamlit.
Why it matters
This open-source tool can help companies gain deeper insights into customer sentiment and feedback from call recordings, enabling them to improve customer experience and support.
Key Points
- 1Leverages Whisper for speech-to-text transcription of call recordings
- 2Uses BERTopic for topic modeling and sentiment analysis on the transcripts
- 3Integrates the analysis into a Streamlit web application for easy visualization
Details
The article walks through the process of building an AI-based customer sentiment analysis tool for call recordings. It starts by using the Whisper speech recognition model to transcribe the audio recordings into text. The transcripts are then fed into the BERTopic topic modeling and sentiment analysis tool to extract key topics and determine the overall sentiment expressed by customers. Finally, the analysis results are integrated into a Streamlit web application, allowing easy visualization and exploration of the insights. This end-to-end solution can help businesses better understand customer feedback and pain points from their call center interactions.
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