Product - Sentiment

Detect sentiments in audio with our Sentiment API

Never miss a feeling with our Sentiment API – track and analyze sentiment for any transcribed media.

Great insight however you slice it

Segment Analysis

Each transcript is split into segments, with the sentiment of each of these segments highlighted. A conversation might go from negative, to neutral, to positive, and back again. Spot the shift, and explore the context. 

Speaker Sentiment

If you’re using Speaker Diarization where different voices are used to identify speakers, you can also access the overall sentiment of a given speaker for the interaction.

Overall Sentiment

If you’re looking for a high-level view of an interaction, this will give you an overall sense of the sentiment of the media. Good, neutral or bad, you’ll see at a glance.

Confidence Scores

Language is nuanced, so we also provide you with some important context for any sentiment, indicating a level of confidence in our classification.

Sentiment has landed!

Speechmatics' Sentiment API is available for you to test, for free.

Integrate in seconds with a simple API.

FAQs

What if the model predicts the wrong sentiment label for a sentence?

Speechmatics provides a confidence score that indicates the confidence of the model in the detected sentiment. It's recommended to take the context of the transcript into account and to validate the sentiment analysis results with human judgment when complex conversations are involved.

Does sentiment analysis increase the time to process?

Yes, sentiment analysis will mean that it will take longer for your transcription to be returned. However, it will only take a few seconds for a 2-hour file. It is designed to be fast and efficient, but processing times may vary depending on the size of the audio file. If you experience longer processing times than expected, don't hesitate to contact our support team.

What if the sentiment analysis results aren't consistent with my expectations?

The Sentiment Analysis model is based on the interpretation of the transcript and may not always accurately completely capture the intended sentiment of the speaker.

It's recommended to review the context of the transcription in relation to the sentiment received first. You should also review the confidence score to understand how confident the model was about its prediction. A low confidence score can mean that it’s not clear with the prediction.

Interested in learning more about our Sentiment API?

Schedule a demo with our specialists who can answer all your questions.