Text Analytics vs Text Mining

Rebekah Carter

Is there a difference between text analytics and text mining?

Text Analytics vs Text Mining

For decades, companies and business leaders have been looking for better ways to derive insights from conversations and interactions. Text offers a valuable source of information, often better suited to things like machine learning and AI algorithms than speech or images. However, as the digital landscape continues to evolve, our options for assessing text are growing.

Today, it’s possible to turn speech into text for deeper insights into customer emotion. Devices can even understand slang and miss-spellings in text, to drive more accurate overviews of trends.

If you’ve ever explored the world of text evaluation before, you may have encountered two distinct terms: text analytics and text mining. The question is, are these two terms synonymous, or do they represent different practices?

The Difference Between Text Mining and Text Analysis

Some people believe that text mining and text analytics are essentially the same thing. Both tools leverage natural language processing (NLP) and other technologies to transform unstructured information in documents and databases into structured data (suitable for analysis).

Experts in analytics say that “text mining” is a term most commonly used in the modern world as new disciplines and artificial intelligence continue to evolve. Text mining uses things like machine learning and natural language understanding to pull information about sentiment, emotion, and more out of structured data. A text mining solution could theoretically identify if a customer is satisfied with a service by analysing reviews, surveys, and feedback.

Text analytics, on the other hand, might look at the patterns and trends that appear in structured text. For instance, with text analytics, you could predict a spike in demand for a specific product by looking at the number of times a product name has been mentioned online in a certain time.

Text analytics is a concept developed within the field of computational linguistics, capable of encoding human understanding into linguistic rules. Analytics and text mining offerings are often used alongside data visualisation techniques and AI suggestions to support quicker decision making.

Gaining a Better View of Text Data

Text mining and text analytics both seek to solve similar problems, though often through different techniques. These complementary technologies help to extract meaning and insight from text, so companies can make better decisions about what their customers need, and what kind of changes are happening in the marketplace. Many organisations with comprehensive analytics strategies will access tools that offer a combination of text mining and analytics features.

When companies are able to analyse and understand structured and unstructured textual data correctly, the benefits can be huge. These services provide deeper insights into customer trends, service quality, product performance, and more. They can help enhance business intelligence, reducing wasted resources and increasing productivity.

In the research landscape, text analytics and mining can help researchers to extract a huge amount of information from pre-existing documentation and literature in a shorter period of time. These practices form an essential part of a full evaluation and analytics strategy.



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