At Context Analytics, we have spent years helping clients quantify unstructured information across news, filings, and social media. But one major channel has remained difficult to measure at scale: audio.
That is now changing.
We are excited to expand our alternative data capabilities with our Podcast Sentiment Analysis Feed, designed to transform podcast discussions into structured financial intelligence. The goal is simple: help research teams capture what is being said about public companies across one of the fastest-growing and most influential media formats.
Why podcasts matter
Podcasts have become a meaningful source of information, commentary, and narrative formation across business, finance, technology, and current events. They are long-form, conversational, and often feature investors, executives, founders, analysts, and subject-matter experts discussing companies and sectors in far more depth than a headline, tweet, or short-form post allows.
That makes podcasts a uniquely rich source of market context.
For financial firms, this matters because audio content can shape perception, reinforce narratives, and influence how companies are discussed across the broader information ecosystem. Yet until now, spoken content has been much harder to convert into structured, research-ready data.
What the product is
Our Podcast Sentiment Analysis Feed converts raw podcast audio into usable, ticker-linked intelligence.
At a high level, the product ingests podcast content, converts audio into text, identifies company and ticker mentions, analyzes how those companies are being discussed, and outputs structured data that clients can integrate into existing workflows. The result is a new layer of alternative data that brings audio into the same analytical framework as news, filings, and social sentiment.
This is about more than simply monitoring podcasts. It is about making a historically hard-to-measure channel searchable, quantifiable, and actionable for financial research teams.
What makes the feed valuable
One of the biggest opportunities in podcast data is that it captures a different kind of signal than most traditional text sources.
Podcast conversations tend to be longer, more nuanced, and more reflective than fast-moving social posts or breaking headlines. They are often consumed over days and weeks rather than in seconds, and they can reveal how companies, sectors, and themes are being discussed in a deeper, more sustained way. That makes podcast data especially useful for understanding narrative development over time.
In other words, podcasts help answer a different question: not just what happened today? but how is this company being talked about, framed, and understood across influential long-form media?
Core product features
Why this matters now
Audio has become too important to ignore. Podcasts are now a mainstream source of business and financial commentary, shaping how company narratives are shared and absorbed across the market.
Context Analytics’ Podcast Sentiment Analysis Feed is designed to close the gap by turning long-form audio into structured, scalable intelligence. With broad podcast coverage, company-level tagging, sentiment analysis, qualitative summaries, and 10+ years of historical depth, it adds a new layer of alternative data for research and investment teams.
Podcasts are no longer just background media. Now they can be measured.
Interested in adding podcast sentiment to your workflow? Contact Context Analytics to learn more.
