Identify podcast transcript sentiment
using AI
Below is a free classifier to identify podcast transcript sentiment. Just input your text, and our AI will predict the sentiment of the podcast transcript - in just seconds.
Contact us for API access
Or, use Nyckel to build highly-accurate custom classifiers in just minutes. No PhD required.
Get started
import nyckel
credentials = nyckel.Credentials("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
nyckel.invoke("podcast-transcript-sentiment", "your_text_here", credentials)
fetch('https://www.nyckel.com/v1/functions/podcast-transcript-sentiment/invoke', {
method: 'POST',
headers: {
'Authorization': 'Bearer ' + 'YOUR_BEARER_TOKEN',
'Content-Type': 'application/json',
},
body: JSON.stringify(
{"data": "your_text_here"}
)
})
.then(response => response.json())
.then(data => console.log(data));
curl -X POST \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_BEARER_TOKEN" \
-d '{"data": "your_text_here"}' \
https://www.nyckel.com/v1/functions/podcast-transcript-sentiment/invoke
How this classifier works
To start, input the text that you'd like analyzed. Our AI tool will then predict the sentiment of the podcast transcript.
This pretrained text model uses a Nyckel-created dataset and has 34 labels, including Affirmative, Agitated, Agreeable, Appreciative, Calm, Content, Contradictory, Critical, Despairing and Disagreeable.
We'll also show a confidence score (the higher the number, the more confident the AI model is around the sentiment of the podcast transcript).
Whether you're just curious or building podcast transcript sentiment detection into your application, we hope our classifier proves helpful.
Recommended Classifiers
Need to identify podcast transcript sentiment at scale?
Get API or Zapier access to this classifier for free. It's perfect for:
- Listener Sentiment Analysis: This use case allows podcast producers to gauge the emotional response of their audience by analyzing transcript sentiments. By identifying positive, negative, and neutral sentiments, creators can tailor future episodes to better meet listener preferences.
- Content Improvement Feedback: Podcasters can use sentiment analysis to pinpoint specific segments of episodes that evoke strong reactions, be they favorable or unfavorable. This feedback can inform content edits and highlight topics that resonate with listeners.
- Marketing Strategy Optimization: Brands can leverage sentiment data from podcast transcripts to fine-tune their marketing strategies. By understanding audience sentiments towards certain topics or products discussed in episodes, advertisers can create targeted campaigns that align with listener interests.
- Competitor Benchmarking: Businesses can analyze competitor podcast transcripts to gain insights into audience sentiment surrounding rival content. This analysis can help strategists identify strengths and weaknesses in competitors’ presentations, allowing them to adjust their offerings accordingly.
- Trend Identification: By tracking sentiment trends over time, organizations can identify shifts in public opinion related to specific themes or issues discussed in podcasts. This data can be invaluable for policymakers and businesses looking to stay ahead of emerging trends.
- Audience Engagement Monitoring: Podcast analytics platforms can implement sentiment analysis to provide podcasters with real-time feedback on listener engagement during live shows or interactive segments. This allows for quick pivots in content delivery based on audience reactions.
- Custom Recommendations for Content: Using sentiment analysis, platforms can recommend personalized podcast episodes to listeners based on their previously expressed sentiments. This enhances user experience by providing tailored content that aligns with individual preferences and emotional responses.