Making the most of sentiment analysis

Thanks to machine learning, companies can now begin to put the ability to determine the emotional value of communication to work. Here’s how organizations can capitalize.

Making the most of sentiment analysis
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Sentiment analysis is beginning to prove its mettle in the enterprise. This analytic technique, which enables companies to determine the emotional value of communications, is finding traction in a range of use cases, from meeting transcription to customer service and feedback.

These days, sentiment analysis relies largely on supervised or semi-supervised machine learning algorithms. All the big cloud players offer sentiment analysis tools, as do most major customer support platforms and marketing vendors. Conversational AI vendors also include sentiment analysis features in their wares.

But making the most of sentiment analysis requires a curious blend of art and science. Here is a look at how some organizations are putting sentiment analysis to beneficial use.

Underscoring importance in transcription

Most virtual meeting platforms offer transcription services. In fact, voice recognition is built into a lot of what Microsoft and Google offer out of the box. Zoom also plans to offer live transcription this fall but, until then, there are third-party services such as Otter AI.

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