The artificial intelligence of the Yva platorm uses NLP (Natural Language Processing) to recognize the presence of sentiment, positive, negative or conflicts in communications between employees.

The information contained in text messages is extremely sensitive to the business of our customers. With this awareness, we deliberately designed Yva in such a way that all text analytics are implemented as a classification. The system has a task: to understand whether the text contains a set of high-level features that can give useful information about the performance of individual employee of the company, about their contribution to a healthy atmosphere in the team, about the actual (not nominal) place of the employee in the business processes of the enterprise.

Yva is designed in such a way that text messages are not stored in the platform, so they cannot be read or stolen, they only exist in the information systems of the customer's company. It is absolutely safe to trust Yva to “read” the text.

We have developed and patented a two-step procedure for working with text. Thanks to it, we can use already trained classifiers or train new ones even without the text itself.

In the context of a company's business processes, the "Message sentiment" assessment helps to determine the health of an entire organization. And it is only the informative part of messages that is being analyzed. It means that all greetings and courtesies are not considered as factors affecting the positive or negative sentiment of the message as a whole.

If there is both negative and positive in one sentence, Yva considers the entire sentence to be negative.

Examples of negative:

  • The deadline is approaching, but nothing is ready.

  • You didn't answer the request.

  • You exceeded the term of contact payment.

Examples of positive:

  • I am up for communication, for me it is always interesting to talk to colleagues.

  • In general, the expressions are good, we want to cooperate.

  • I hope we will go on at this pace and finish everything timely and on a high level.

Metrics of an employee communication with sentiment are divided into three blocks:

  • Metrics for all contacts, that is, any communication of an employee,

  • Metrics for internal contacts, that is, communication within the organization,

  • Metrics for external contacts, that is, communication with people outside the organization.

For each block of metrics Yva calculates:

The Number of received emails with sentiment

The Number of sent emails with sentiment

The Average response time to emails with sentiment

The Percent of responses to emails with sentiment