Text Analytics and Natural Language Processing in the Era of Big Data

A huge significant growth in the volume and range of data is because of the to the unstructured text data being accumulated. As a matter of fact, about 80 percent of all your personal data is unstructured text data. Organizations like Provalis research solicit a huge chunk of documents, emails, social media, and other text-based information to get to learn their customers more, deliver customized services, or comply with federal regulations. But, most of this data is unused and untouched.

Text analytics, with the help of natural language processing or NLP, holds the key to unlocking the treasures of business value within these huge chunks of data. In these times of huge data assets, the ideal platform lets the businesses to use their data lake fully and make the most of the latest parallel text analytics and NLP algorithms. In such a situation, text analytics delivers the integration of unstructured text data with structured data (for instance, customer transaction records) to provide deeper and more accurate depictions of business operations and its customers.

What is Natural Language Processing (NLP) and Text Analytics?

It is a scientific discipline that is related to making the natural language fully accessible to the machines. NLP addresses tasks like identifying sentence boundaries in documents, extracting relationships from documents, and searching and retrieving of documents, among others. NLP is a way to facilitate text analytics by building a structure in the unstructured text to further enable the analysis.

Text analytics is a method of extracting useful information from text sources. It describes tasks from annotating text sources with the help of meta-information like people and places present in the text to a variety of models about the text (for instance, sentiment analysis, text clustering, and categorization). In other words, the term document is an abstract notion that can depict any coherent piece of text in a wide collection like a single blog post in a collection of WordPress posts, a New York Times article, a page on Wikipedia, and the like.

When it comes to conducting the text analytics task, a data scientist may develop features or independent or explanatory variables that can easily help in describing a myriad aspect of the document. For instance:

  • The document is about ‘sports’ or ‘politics’.
  • The phone call contains a lot of negative language.
  • The website mentions a particular product.
  • The tweet describes a relation between a product and a problem with the product.
  • The author of the blog post is likely female.
  • The email breaks compliance because it reveals personal information.

This is how text analytics is beneficial for the following verticals:

  • Finance: Search, compliance, entity matching, call-center analytics etc.
  • Insurance: Sentiment analysis, problem topic identification etc.
  • Media: Social media analytics, broadcast etc.
  • Retail: Brand analysis according to the customer feedback
  • Process control: Problem augmentation from operator notes
  • Energy: Price and demand forecasting
  • Oil and gas: operator comments analysis
  • Legal: Search, relationship extraction, document clustering etc.