Sentiment Analysis with Hugging Face and Deepgram

In this tutorial, the author demonstrates how to create a sentiment analysis tool using Hugging Face and Deepgram APIs. The project involves setting up a Jupyter notebook with necessary packages such as transformers, numpy, pandas, matplotlib, seaborn, scipy, deepgram-sdk, python-dotenv, torch, and pytube. The process includes transcribing audio with Deepgram, setting up the sentiment analysis pipeline with Hugging Face, converting output into one composite sentiment score, turning data into a sentiment chart, and smoothing the chart for better visualization. This tool can be used to analyze any video or audio clip's sentiments over time, providing insights into customer perceptions, brand reputation, market trends, and more.

Use this post, company, and trend context to find content marketing opportunities, perform competitive analysis, or address product feature gaps via the Plushcap MCP server or the Plushcap API.