Natural Language Processing and Text Mining: Discover the Main Differences
When it comes to analyzing unstructured data sets, a range of methodologies/are used. Today, we’ll look at the difference between natural language processing and text mining.
To describe text mining, often referred to as text analytics, I like this definition from Oxford: “the process or practice of examining large collections of written resources in order to generate new information.” The goal of text mining is to discover relevant information in text by transforming the text into data that can be used for further analysis. Text mining accomplishes this through the use of a variety of analysis methodologies; natural language processing (NLP) is one of them.
Although it may sound similar, text mining is very different from the “web search” version of search that most of us are used to, involves serving already known information to a user. Instead, in text mining the main scope is to discover relevant information that is possibly unknown and hidden in the context of other information .
Natural language processing (or NLP) is a component of text mining that performs a special kind of linguistic analysis that essentially helps a machine “read” text. NLP uses a variety of methodologies to decipher the ambiguities in human language, including the following: automatic summarization, part-of-speech tagging, disambiguation, entity extraction and relations extraction, as well as disambiguation and natural language understanding and recognition.
To work, any natural language processing software needs a consistent knowledge base such as a detailed thesaurus, a lexicon of words, a data set for linguistic and grammatical rules, an ontology and up-to-date entities.
Today, NLP software is a “shadow” process running in the background of many common applications such as the personal assistant features in smartphones, translation software and in self-service phone banking applications.
The difference between natural language processing and text mining
What’s important is how powerful text mining and NLP can be when employed together. Think of it this way: Because it’s impossible to read all of the information ourselves and identify what’s most important, text mining applications (using NLP) does this for us. More than a search tool that simply returns a list of sources that match our request, text mining tools go even further to give us detailed information about the text itself (meanings, etc.), and reveals patterns across the millions of documents in your data set.
Text analytics and NLP examples
Text mining and NLP are commonly used together for different purposes, and one of most common applications is social media monitoring, where an analysis is performed on a pool of user-generated content to understand mood, emotions and awareness related to a topic.
expert.ai’s marketing staff periodically performs this kind of analysis, using expert.ai Discover on trending topics to showcase the features of the technology. Follow link to view some of our IQ series reports.
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Originally published April 2016, updated May 2020