Sentiment Analysis

Know the tone of a message before reading

Sentiment analysis is becoming increasingly popular among businesses of all sizes. Use Levity to extract the tone of messages instantly and at scale.

Why bother?

Whether you want to set up automated social media listening, track how your products perform on text reviews, or just auto-delete angry emails so that you can enjoy life in peace: It all starts with sentiment analysis.

Conceptually, sentiment analysis is a method to understand the tone of any text message. More precisely, messages are classified as positive, neutral, and negative. Most companies use it for understanding their customers better and to make this more tangible, here are a few more examples:

  • Prioritize customer support tickets: Depending on your strategy, you may want to handle negative customer feedback with higher priority or automatically escalate it to the supervisor level.
  • Customer satisfaction: Track how feedback trends over time, send automated thank-you-notes to friendly reviewers, prioritize negative feedback of top customers, or tag open-ended questions from Net Promoter Score analyses (the last one is so popular that we have a separate page on it).
  • Enhance customer segmentation: Pairing sentiment analysis with demographic and other quantitative data helps to better understand a particular segment. For example, do customers who spend less time on your site feel more negatively (and therefore it is a barrier to them spending more)?
  • Track how people feel about certain actions: Remember when Google changed all their product logos into a single design? Well, the feedback was plenty, and Google sure didn't need to run sentiment analysis. But for a lesser-known company (i.e. all others) or product, getting a numerical understanding of how people feel about a certain change may be crucial to anticipate future events or change course early.
  • Brand monitoring: Quickly react to (particularly negative) mentions all over the web.

With Levity, you can do all this with ease. All you need to do is connect the required data source(s), plug in a pre-built or customized model for sentiment analysis, and specify what should happen with the predictions – done.

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