Category Archives: Emotion Recognition

MeaningCloud blog entries about emotion recognition and analysis

Recorded webinar: Deep text analytics to transform customer feedback into action

Last April 29th we delivered our webinar “Leverage deep text analytics to transform customer feedback into action”. Thank you all for your interest.

In it we explained how to use Meaning Cloud’s products in a synergic way to analyze your customer feedback through surveys, contact center interactions and social media, and level up your customer insights.

During the session we covered these items:

  • Leveraging unstructured customer feedback: benefits and challenges
  • Text analytics to the rescue… but with limitations
  • How to use deep text analytics to extract more actionable insights
    • Pre-made Insights
    • Adaptation
    • Development
  • understand the opinions, perceptions, emotions and intentions of your customers.

Interested? Here you have the presentation and the recording of the webinar.

(También presentamos este webinar en español. Tenéis la grabación aquí.)

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Leverage deep text analytics to transform customer feedback into action (webinar)

Customer FeedbackOne of MeaningCloud’s goals is providing you with the best text analytics technology to help you better understand your customers and in recent times we have been launching products in this area: Voice of the Customer, Emotion Recognition, Intention Analysis.

But maybe you haven’t thought about how to use these products in a synergic way to analyze your customers’ feedback through surveys, contact center interactions and social media, and transform that feedback into action.

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Automatic Emotion Recognition

In our previous post, we made an introduction to emotion recognition to celebrate the release of the publication of our automatic Emotion Recognition pack. We talked about how emotions play a prominent role in the individual and social life of people and how they have a great impact on their behavior and judgements.

We also saw how thanks to Natural Language Processing we can extract the underlying emotions expressed in a text in a fast and simple way and we saw how useful it can be in multiple scenarios.

In this post, we are going to explain in depth how to get the most out of our automatic emotion recognition pack. We will talk about the criteria and considerations we’ve followed in our approach.

Emotion Recognition

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Introduction to emotion recognition in text

Emotions govern our daily lives; they are a big part of the human experience, and inevitably they affect our decision-making. We tend to repeat actions that make us feel happy, but we avoid those that make us angry or sad.

Information spreads quickly via the Internet — a big part of it as text — and as we know, emotions tend to intensify if left undealt with.

Thanks to natural language processing, this subjective information can be extracted from written sources such as reviews, recommendations, publications on social media, transcribed conversations, etc., allowing us to understand the emotions expressed by the author of the text and therefore act accordingly.

Emotion

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