The advent of data warehouses gave businesses the power to collect, store, and analyze information from multiple corporate systems in a single, high-performance environment. However, business managers were limited to analyzing only structured data. Structured data consist of tidy or fixed answers and numerals arranged in rows and columns. These data are easily stored, categorized, queried, reported, and rolled up by a database. Text analytics opens the floodgates to new insights by allowing companies to analyze unstructured, free-form data in the same way structured data has been analyzed in enterprise data warehouses.

a large amount of unstructured, free-form text. For example, notes entered from a call center, open-ended responses on a customer survey, and comments posted on the Internet all are defined as unstructured text. Text analytics, also known as text mining, is a technology that turns that unstructured information into structured information so that it can be properly analyzed by business intelligence systems. There are many approaches and techniques used to turn text into structured information. Each approach has varying levels of accuracy and utility. In this article, we will explore those techniques and how they can be used in combination to uncover hidden insights stored within the text.

One of the most common things that can be learned using text analytics is when a customer expresses some sort of positive or negative emotion in conjunction with a company or brand interaction. Considering all of the things that could be expressed by customers in a free-form comment, general sentiment is relatively easy to discern since the way that people describe being upset or unhappy is universal.

However, the most valuable insights gained from customer comments are those that are called actionable insights. Actionable insights actually point to a specific condition or state within a customer’s experience that the company could have an immediate impact on, such as a specific product problem. Another example is an issue with an operational procedure or policy that causes some frustration in customers or perhaps a poor interaction with a customer service agent regarding a refund. Unlike expressions of general sentiment, these are specific types of insights that can point to specific actions a company can take to keep customers from leaving or to directly increase loyalty and satisfaction.

Questions

1. Why has text analytics become almost a necessity today?

2. Please describe the different approaches to text analytics or processing?