What is Text Mining?

Text mining is the process by which analytics suites gain actionable insights from data in the form of text. This sort of analysis involves taking unstructured text and organizing it within a structured database to simplify the process of studying it.

The structuring process is carried out by parsing the texts, as well as data scrubbing which removes irrelevant points and fixes any potential errors in text. This type of data mining looks for high-quality data, which is defined by its relevance and novelty.

See it in action:

There are several techniques data scientists and organizations use to prepare text for mining, including categorization, text clustering, and concept extraction. Additionally, there are more in-depth linguistic tools that are useful, including entity relation modeling.

Once data is organized, text is analyzed using a combination of linguistic and analytic tools including pattern recognition, information extraction, and natural language processing.

For organizations, analyzing text is vital regardless of industry or field. Many times, clients provide data that is not easily sortable or comes in qualitative forms, making it difficult to analyze. Text mining and text analytics provide a structure and offer a clear methodology to convert text to data that can be more easily analyzed and visualized.

What Can I Use Text Mining For?

There are several industries that can successfully use text mining to enhance their analytics. In retail analytics, for example, user feedback does not necessarily arrive in neat data sets, but instead in the form of opinions, comments, and complaints.

Text mining can simplify the process of finding insights in this unstructured data and provide clearer data visualization. This can include data from multiple text sources as well.

See it in action:

Retail Distribution Management Dashboard

One of the more common text mining examples is their inclusion in a marketing analytics dashboard. A major metric for success is the type of conversations around a marketing campaign or a brand.

By parsing through text from social media, websites, and other text data sources, companies can determine key emotions, possible hashtags, general inclinations, and approval for their efforts.

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Marketing Dashboard Example

Similarly, marketing departments also utilize text mining to find keywords that are more widely used to increase their visibility on major search engines like Google and Bing. Search engine optimization depends heavily on text mining to find the most commonly used search terms to help enhance content and a websites’ visibility.

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