Three reasons why you need V.E.R.N.
Real TimeIntegrate V.E.R.N. for instantaneous analysis wherever you need to know how people are feeling.
Sentence Level AnalysisAnalyze each sentence for emotion, and find exactly how your users feel.
Actionable ResultsGet "Anger," "Sadness," and other emotions not just positive, negative, neutral or "mixed." Get started immediately, no need for the model to "learn."
Real-time communications analysis with emotional intelligence
Do you want to know what your clients feel about you? Would you like to get this information as it happens? V.E.R.N. removes a delay between data collection and execution.
Connect direct to V.E.R.N.
Connect through our API and
get real-time results for your communications software. Get all of the emotions we track and the latest updates to ensure the most accurate analysis. You'll love to use our dashboard to create and set initiatives, and monitor the emotion in your user's communication.
Get V.E.R.N. from an AI Marketplace
Enable V.E.R.N. for your ModelOps. Secure system solutions enable use of V.E.R.N. in sensitive use cases. Our AI Marketplace partners offer V.E.R.N. in an easy-to-use solution.
Domain and Personal level analysis
Users aren't all the same. A plumber and a podiatrist speak a slightly different language. We'll work with your company to create custom framing to enable your analysis to even greater accuracy.
As humans, we include a lot of emotional content when we communicate with one another. V.E.R.N. allows us to not only have a better understanding of the emotions being conveyed but also allows for us to develop tools to help us respond. V.E.R.N. results include a confidence rating on a scale of 0 – 100% […]
In part one of our series, we compared sentiment analysis to emotion recognition in order to show the differences between the two approaches. We’re back to do another comparison and talk a little about the methodologies and how they differ. First, a quick refresher: Sentiment analysis categorizes and analyzes text to determine if the writer’s […]
Last week we discussed the VERN emotion recognition model. We talked about how the world doesn’t have a clear idea of how many emotions there are, what they are, and how the heck they can even be detected. We provided some clarity on our model and how it relates to finding emotion in communication. Today […]
Last week we compared sentiment analysis to emotion recognition software, using VERN. We saw that sentiment analysis rated content as “positive,” “negative,” “neutral,” and “mixed.” Some software rated content with a magnitude, indicating the strength of the “positive,” “negative,” “neutral,” or “mixed.” In some cases, it is accurate. In others, it was not. Emotion recognition looks […]
Emotion recognition software like VERN looks for emotional clues in text. What people say, or write down and send to one another, through emails or chats or text messages often contain emotions. Not just strictly sentiment, but the emotions that we are familiar with and would recognize someone expressing them. Those that are familiar with […]