#Tech | 7 min read | Updated: 8/15/2022

Natural Language Processing vs Text Mining

Updated: 8/15/2022
, Chief Strategy Officer of Sloboda Studio
7 min read

The expansion of the digital universe is one of the most significant things that mankind has faced. Big Data is rising.

It is a promising but dangerous IT field – we have learned how to collect and store terabytes of data, but still barely understand how to process it. So it’s time to talk about natural language processing vs text mining.

As a report by EMC says, less than 1% of the world’s data is analyzed and processed. Taking into account that the industry keeps growing, we can assume that the percentage will reduce in the future.

Processing huge textual data is a task that is impossible to perform manually. We need to automate this type of process in order to extract the essence of the global data collected and learn its value.

Today we will explore the specifics of the best methods of data processing and compare the benefits of natural language processing and text mining.

Natural Language Processing vs Text Mining: Brief Intro

Our first step towards understanding the concepts of NLP vs text mining is basic familiarity with these methods. Let’s start with NLP, or natural language processing.


NLP is a branch of artificial intelligence that deals with communication.

This is a method that allows machines to create (natural language generation) and analyze (natural language understanding) the human language. NLP is able to process various types of speech, including slang, dialects, and even misspellings.

Machine learning builds the basis for this method.

An ML system simply stores words and word combinations along with sentences or even whole chapters and books. It creates a special type of database.

The ML system requires accounting for the following things for correct processing:

  • Grammatical rules
  • People’s linguistic habits

The machine uses these things to create patterns and find the needed results. For example, the sentence “I go to the park” provides information about:

  • the action, and each time this action will be mentioned the machine will use the word combination “I go”.
  • the place called “park”, which could be potentially replaced by another word according to the situation.

Where can you encounter the NLP method? There are some well known places where it’s used:

Search Engines

Every time you google something, you upload data into the search engine. It looks for connected results, and when you click on a link, the system decides everything was done correctly and uses your choice to provide better results in the future.

Intelligent Chatbots

The NLP algorithm waits in the background for a special trigger to register that you need it. The trigger awakens a chatbot program integrated into your communication channel or website and guides you through the processes.

Spellcheck Apps

Do you use tools like Grammarly to check whether your vocabulary is OK? The spellchecking apps have huge databases of words, word combinations and rules, and when you type a word incorrectly, the NLP system suggests a correction.

Text Mining

Text Mining is a subtype of global data mining science. This is a field that includes data search and retrieval, data mining and machine learning methods. Today, more than 80% of organizations worldwide use textual information actively.

And text mining provides valuable tips on how to exchange and process it.

Text mining extracts the information from text files. The automatic analysis of word documents, emails, social media posts or web articles provides the needed information in an optimized way.

When we deal with quantitative data, there is nothing complicated about it, and we have invented numerous tools and machines for calculations and measurements. But text mining provides qualitative data analysis. Text mining helps to distinguish between structured data and unstructured text.

What can it help you with?

  • Extracting the patterns: text mining analyzes a huge amount of data and helps with identifying the patterns.
  • Reviewing the literature: the text mining system has the ability to process the text, define the theme and subjects, highlight the most commonly used terms or the most popular topics, etc.
  • Testing the concepts: it can be used to test hypotheses and confirm them.

7 Significant Points in the Text Mining vs Natural Language Processing Comparison

We’ll describe the 7 main differences between text mining and natural language processing below:

1. Key Goal

The NLP system allows understanding what actions and senses hide behind human languages. It analyzes semantics and grammatical structures and improves the process of work. NLP has the ability to recognize text and speech. It is responsible for making the interaction with machines more simple and convenient for people.

Text mining deals with text quality evaluation. It works with both structured and unstructured data. This type of system does not consider semantic features, but can easily deal with the following tasks:

  • Information patterns search.
  • Matching structures identification.

2. Development Process

The development process differs for each of the methods.

Have a look at the basic steps you need to take to develop an NLP solution:

  • Define the problem and decide on the type of data you need to analyze.
  • Analyze the qualitative and quantitative features of the problem.
  • Create the reference corpus.
  • Proceed with preprocessing and feature engineering.
  • Decide on computational techniques.
  • Develop the decision algorithm.
  • Run the model, test and improve it.

For text mining, the process is almost the same. However, you need no reference corpus for text mining system development.

  • Think over and program the basic features.
  • Decide on a computational technique.
  • Use a rule-based or simple machine learning statistical model.
  • Deal with the special presentation layer where the findings from mining appear.
  • Run the model, test it and measure the system accuracy.

3. Required Tools

Machine learning technologies serve as tools for both of these methods, but there are some specific tools as well.

To build a high quality NLP system you need to have:

  • Proficiency in neural networks and deep learning.
  • Familiarity with toolkits such as NLTK.

To get a text mining system, you should be familiar with:

  • Techniques such as Levenshtein Distance, Cosine Similarity or Feature Hashing.
  • Text processing programming languages such as Perl or Python.
  • Statistical models.

4. Scope of Work

NLP works with any product of natural human communication including text, speech, images, signs, etc. It extracts the semantic meanings and analyzes the grammatical structures the user inputs.

Text mining works with text documents. It extracts the documents’ features and uses qualitative analysis.

5. Results Achieved

NLP provides the understanding of the feelings described, grammatical structure and semantic meaning. These results allow a seamless translation of the text to other languages.

Text mining shows the relationships between the words in the text. It analyzes word frequencies and patterns used. It is an irreplaceable method for identifying the statistical features.

6. Accuracy of the Methods

Accuracy is a rather controversial issue.

Let’s take a look at an example: you try to analyze the accuracy of the translation of an extract from your diploma from English to Chinese. For that, you need two native speakers with brilliant knowledge of foreign languages to conclude whether the translation is accurate.

Here is the problem we have with the NLP system: we cannot automate the accuracy measurements yet; human participation is needed.

Text mining accuracy can be measured using automated mathematical methods. It is easier to evaluate its performance than analyze the NLP system’s accuracy.

7. Current and Future Applications

The most important part of the comparison between text mining and natural language processing is the potential applications.

NLP now successfully serves as a part of speech recognition and survey systems. It is an essential part of translation tools and it helps with summarization and classification of texts.

Remember Sophia, the humanoid robot? With a powerful NLP system, you can build a robot that can understand people and interact with them in any language.

Besides, it will be a significant element of universal translators. Intelligent NLP systems can produce titles for given texts, or even entire texts on a given topic.

Text mining will be useful for SEO and website marketing purposes. It is great for contextual advertising and business promotion. It can enrich the content posted on your website and analyze data collected from your website or social media channels in the best way.

Besides, it is good for security. A text mining system allows filtering out spam and detecting fraud.

Final Thoughts

Both natural language processing and text mining provide the following advantages:

  • Saving time and resources.
  • Demonstrating much higher efficiency than human brains.
  • Tracking information flow.
  • Extracting valuable data, etc.

Natural language processing is able to recognize and process speech, text or even images. It dives into grammatical and semantic particularities to provide the most accurate results. It helps to reveal the meaning that hides behind the grammatical structure.

Text mining allows extracting the details from the available data, both structured and unstructured. It cannot help with understanding the information conveyed, but allows providing exact information from the text.

So, text analysis or natural language processing?

To answer this question, you need to clearly understand what your goal is. Depending on what purpose you have, you can pick the method that suits your needs best. Additionally, NLP and text mining can be employed together. They augment each other and may bring great value together.

Have you tried one of these methods? Was this article useful to you?

If you still have any questions, just drop us a line and we’ll find the answers together.

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