Text Mining vs. Natural Language Processing


In the 21st century data has become as much of a commodity as oil was in the 20th century. As businesses become more reliant on data-driven decisions, the importance of utilizing the right type of data methods to process the right information for businesses becomes paramount. Access to unstructured data has emphasized the need to leverage large data sets. Businesses are no longer able to process large sets of textual data manually and have come to rely on automation. To learn how to best extract value from data, it is important to understand the difference in benefits of Natural Language Processing (NLP) versus text mining.

Natural Language Processing (NLP) is a subset of AI in which computers can analyze and interpret human language in an efficient and useful way. It is a way to get a human-level understanding of the language for machines. Using Natural Language Generation (NLG) and Natural Language Understanding (NLU), NLP can process different types of speech including misspellings. It does that by utilizing Machine Learning (ML), a system that relies on special types of databases to extract language.

Text mining is a subtype of data mining. It focuses on data mining and ML methods as it relates to textual information. More specifically, it extracts the information from text files. A wide array of text files can be used in text mining including structured and unstructured data in emails, social media posts, and web content. Text mining works for qualitative data analysis and it helps distinguish between different types of data.

Differences Between Text Mining v. Natural Language Processing

1. Purpose

NLP and text mining differ in the goal for which they are used. 

NLP is used to understand human language by analyzing text, speech, or grammatical syntax. 

Text mining is used to extract information from unstructured and structured content. It focuses on structure rather than the meaning of content.

2. Tools

Businesses use different tools when working with NLP and text mining. 

NLP uses businesses leverage advanced ML models, artificial neural networks (ANN), and tools like NLTK in Python. 

Text mining also uses ML models along with statistical models and text processing languages like Perl. A business must invest in the tools necessary for specific data processing to be successful.

3. Capability

NLP is geared towards mimicking natural human communication. It uses text and speech as input to extract grammatical structure and syntax meaning. 

Text Mining is geared towards analyzing qualitative data.

4. Results

 The outcomes for using different data methods differ. 

NLP can be used to extract grammatical structure as well as the sentiment from language. 

Text mining uses statistical indicators like frequency of words, patterns of words, and correlation within words to explain the text.

Differences Between Text Mining v. Natural Language Examples

The major difference between NLP and text mining is in potential application.

Natural Language Processing

In everyday use, NLP can be found in search engines that provide correct answers when users enter queries. It can also be seen in intelligent chatbots that are integrated into communication channels and websites to provide customer service. Another application of NLP is spellchecking apps, tools like Grammarly, have huge databases of words, grammatical rules, and combinations that are powered by NLP.

Text Mining

Text mining is used for SEO and website marketing purposes. It can guide contextual advertising and target promotions. Another application of text mining is analyzing data from website content and social media platforms.


While NLP and text mining have core differences in application, they both provide advantages when used to analyze data for a business. These data methods save time and resources, are more efficient than human intelligence, and track information flow. Gathering insights from structured and unstructured data, NLP and text mining provide valuable insights that move businesses into the future.

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