Podcast listeners tend to be young, affluent, and engaged by long-form content. Semantic search for audio uses natural language processing to automatically analyze and index podcasts by topics, themes, entities, and sentiment without any manual tagging. This allows users to quickly search large audio libraries and access relevant content. The technology provides a dimensional search interface that unlocks more value from audio collections than other analysis tools.

2. Audio Sentiment Analysis
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3. Who listens to Podcasts?
Marketers and podcast producers are always looking to reach unique, targeted audiences.
Marketers are in search of media environments that people find engaging, while podcast
producers are looking to attract audiences that are drawn by relevant content topics and quality
material. 37% of Americans listen to podcasts at least once a month
● 24% of Americans listen to podcasts weekly
● 46% of podcast listeners are between the ages of 18-34
● Podcast listeners are 45% more likely to have a college degree
● Podcast listeners are 37% more likely to have $100,000+ in annual income.
In short, podcasts reach young, affluent audiences who are engaged by engaging, longer content.

4. What is Semantic Search for Audio & Podcasts?
Using Semantic Search for Audio makes your podcast files easily searchable by semantically
indexing the content of your data. Users can search your entire audio catalog for the exact content
they want without any manual tagging on your end.
Semantic search for audio provides customer service teams, podcast producers, marketing
departments, and heads of sales the power to search audio files by specific topics, themes, and
entities. These entities include celebrities, politicians, locations, and more. Semantic search
automatically annotates your podcast data with semantic analysis information without any additional
training requirements. Just plug it in and get to work!
With semantic search, find anything you need at the click of a button. Say goodbye to hours spent
listening to audio files and have access to what you need at your fingertips.

5. What are the benefits of Semantic Search for Audio?
Using Semantic Search for audio analysis, you can easily index, search, and discover topics,
themes, and entities. Using this audio content analytics tool, you can also conduct sentiment
analysis on your audio files to derive sentiment for keywords and topics.
The biggest challenge for quality audio content analysis tools lies in their innate ability to not only
search and organize, but also access assets in a semantically relevant way. Rather than having
your team manually listen to a full audio file and guess at relevant keywords and the emotion behind
the voice, Semantic Search for Audio analysis offers a dimensional search and browsing interface
for operating in podcast collections. This makes it easier than ever before to unlock the potential of
your audio library, no matter how complicated the search seems. There are hundreds of audio
analysis tools out there, but nothing compares to what Semantic Search for Audio can do for you
and your business.

6. How does Semantic Search for Audio find content themes,
topics, and entities in Podcasts?

7. Thank you!
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8. Thank you!
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