We’ve launched Sentiment Analysis, and we’re feeling 😃

We’re now extremely accustomed to ‘social proof’ as a concept. When looking at whether or not to engage or buy from a company, we often sift through testimonials, read Trustpilot ratings, or stumble across hilarious customer reviews like this one for the 571B Banana Slicer:

As a brand or business, this feedback is great, but obviously is only a tiny window into the minds of customers. Reviews tend to skew either extremely positive or negative, since everyone in the middle probably did not have an emotional enough reaction to leave a review in the first place. Reviews are only left by people choosing to take the time to write one.

But what about what people are saying (literally)? Contained within video and audio, or recorded conversations, there might be a blend of different sentiments.

"The build quality of the banana slicer is incredible, but it was delivered in red, not yellow, but it arrived just 20 minutes after I ordered it, which was super valuable as I had a lot of bananas to slice." 

A three-star rating doesn’t capture this nuance, but a 10-minute recorded customer call would. 

Sounds great in principle, but how are you supposed to capture this at scale? If you are a call center fielding thousands of customer calls a day, or week, how can you even begin to make sense of all that nuanced sentiment? 

Well, enter Sentiment Analysis.

Sentiment Analysis: How people really feel, at scale.

Sentiment gives businesses the ability to track and analyze sentiment for any transcribed media via an API call. 

Our Sentiment Analysis uses machine learning to look at complete sentences or segments of an audio transcription and ascribes a positive, neutral, or negative sentiment classification, along with a confidence score between 0 and 1. For example, if a sentence was given a ‘positive 0.92’ classification, that would be a very strong indication that the model predicted the segment to be positive. 

This isn’t the only piece of insight you get with Speechmatics. For any audio transcript, you get several pieces of analysis: 

• 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. This would allow you to see that shift quickly. 

• 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. 

• Channel sentiment: If you’re using multi-channel media for speaker separation, you can receive the overall sentiment for a given channel 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.  

• 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 (between 0 and 1).

To showcase this, we’ve run a news segment from Bloomberg through our Sentiment Analysis:

You can see clearly that the sentiment shifts, even within one speaker’s single answer. And, although the above seems largely positive, it should perhaps not come as a surprise given it’s a news segment, that the overall sentiment of this conversation was negative: 

Specific Use Cases

Any organization looking to explore how their customers are really feeling about their service will find this useful, but here are a couple of specific use cases...