How Text Mining Works with Survey Analysis Software

Open-ended responses can add valuable insights to surveys, but they can also be a challenge to analyze. Responses provided in natural language can be inconsistent and peppered with ambiguities stemming from syntax, semantics and slang.

Manually sifting through the responses is one option, although it’s not necessarily the most efficient one. A faster and easier choice is using text mining features found in many survey analysis software packages.

Text Mining Overview

Text mining essentially does the sifting for you, automatically converting words and phrases in natural text into numeric values. Those numeric values can then be linked with structured data in your database and analyzed using traditional methods. Analyzing survey text data thus becomes as straightforward as analyzing any other data with your survey analysis software.

Text Mining Details

While text mining automates the analysis process, you’re still free to step in at any time. You can manually adjust categories or otherwise refine the results to ensure the highest levels of accuracy and meaning for your specific purposes.

Different survey analysis software may have different text mining capabilities, although many functions are fairly standard across the board. Text mining features are typically designed to help you:

  • Find recurring themes without the need to read the text verbatim
  • Differentiate between negative and positive feedback
  • Quickly create classifications and tags
  • Consistently tag responses
  • Use previously established categories for similar or ongoing surveys

Text mining can be particularly useful for satisfaction surveys, whether they’re geared toward customers, products or employees.

See Also: 7 Things You Need to Know About Survey Analysis Software

Automated Text Tagging

Text tagging is an essential function for prepping natural language responses for traditional analysis, and text mining can take care of the entire process.

One of the most useful text tagging methods is known as “named entity extraction.” Here the natural language text is scanned to pinpoint the names of people, organizations, locations, products or dates. This technique is particularly helpful for determining the pattern of interactions and relationship between named entities.

Programs may not only perform text tagging for you, but they can also come equipped with other features. Some programs may come with pre-built tags that are applicable to most satisfaction surveys. If you’d rather create your own tags, programs may also offer tag-building techniques that further reduce manual tasks.

A built-in tag editing feature is often part of the package, allowing you to conveniently amend tags as needed or desired. One more perk that may be included is translation features, which allows you to prepare and analyze natural language text from a number of different languages.

Getting Started

Even though text mining can make your job easier in the long run, it may still seem a bit overwhelming when you’re first getting started. Most survey analysis software addresses this issue as well, with step-by-step, on-screen guidance for setting up and extracting concepts as well as creating tags and categories.

With text mining capabilities, open-ended survey responses are no longer a time-consuming challenge. They can instead be processed nearly as rapidly and accurately as the rest of your survey data.