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Text Analytics Terms You Need to Know

Whether you’re a seasoned pro or just getting started in the world of customer experience (CX) and employee experience (EX), you need to be fluent in the language of text analytics.

However, that’s more easily said than done. With technology evolving so quickly, it’s hard to keep up with the latest and greatest. That’s why we’ve put together this quick text analytics glossary. Check it out below!

Top Terms

Accuracy: The combination of precision and recall for a given tag or model. 

Emotion: A measure of positive/negative feelings. Must be strong and clear-cut enough to be categorized as a specific emotion.

Human Translation: This translation method has a human translate each comment individually as the customer submits it.

Intent: Intent identifies what the customer is trying to achieve based on their response.

Keyword: A word or term that occurs in unstructured customer feedback data.

Machine Translation: Translation done by a machine that has been trained by humans.

Native Language Model: A text analytics model that is purposely built for a specific spoken language.

Natural Language Processing: A field of computer science and artificial intelligence that draws intelligence from unstructured data.

Precision: Correctness; represents how often a given concept is correctly captured by a specific tag. 

Recall: Coverage; refers to how thoroughly the topics or ideas within a given tag are captured. 

Sentiment: The expressed feeling or attitude behind a customer’s feedback. Categorized as positive, negative, or neutral.

Sentiment Phrase: Also referred to as a Sentiment Bearing Phrase or SBP. A phrase or sentence identified with positive, negative, or neutral sentiment.

Sentiment Score: A measure for both the polarity and intensity of the sentiment within a given comment.

Tag: A label generated from text analytics that groups together similar customer comments around a specific concept or topic.

Text Analytics: The methods and processes used for obtaining insights from unstructured data.

Text Analytics Model: A natural language processing engine that uses tags to label and organize unstructured data.

Theme: A dynamically extracted concept from a collection of comments, generated by an unsupervised machine learning algorithm.

Unstructured Data: Qualitative data or information that is not organized according to an easily recognizable structure. Can include comments, social data, images, or audio recordings

Making the Difference with Text Analytics

We hope this quick glossary helped you on your journey to find the best solution for your business. After all, text analytics make the difference between getting a meaningless score from your data and getting actionable intelligence. And without that intelligence, you can’t make experience improvements in the moments that matter. That’s why it’s so important to get your text analytics right!

If you want to learn more about world-class text analytics solutions, including new approaches like custom layered models and adaptive sentiment engines, you can check out our full eBook on the subject here!

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The InMoment Team

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