I noticed that investment narratives often appear on podcasts before they reach mainstream media. I wanted a systematic way to identify and track them. So I built a pipeline that transcribes podcast episodes, extracts discrete ideas from each transcript, embeds and clusters related ideas into topics, and plots them on a 2D semantic map. The map is built from over 700,000 discrete ideas mined from 66k episodes published in the last five months. One of the harder data-cleaning problems was filtering out AI-generated episodes. Around 15% of the financial podcasts entering the pipeline contained synthetic content. Fully synthetic shows were relatively easy to remove. The harder cases were shows that mixed AI-generated episodes with genuine ones. On the map, you can zoom into any topic, see how discussion volume has changed over time, follow the key developments within it, and listen to the original podcast clips behind it. You can check it out here: https://www.sonicalpha.ai/atlas https://preview.redd.it/rh3fjansy7hh1.png?width=1203&format=png&auto=webp&s=313b90963a9cbf565e35f492ad743938b569ecf2 Would love to get your feedback. Thanks submitted by /u/g_pal
Originally posted by u/g_pal on r/ArtificialInteligence
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