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NLP In Data Science: 4 Natural Language Techniques

NLP in Data Science: If you are preparing to work or already work in  ​​Data Science, know that it has been growing a lot in the market.

However, a field that is still little talked about is Natural Language Processing, which is very important for these professionals.

The so-called NLP – Natural Language Process – is still little debated in the context of Data Science, but we want to change that. With this in mind, we prepared today’s particular article. Good reading!

What Is NLP?

Natural Language Process (NLP) or Natural Language Processing (NLP) is a technique created from the articulation between Data Science, Artificial Intelligence and linguistics, with the objective of “translating” human language for data processing from the construction of text processing models.

Have you noticed that most of the customer service services we use today initially make us go through an automatic response system?

Sometimes, we receive a convenient response to our contact, but on other occasions, it is as if the machine does not understand what we are trying to express.

This is where the studies developed in NLP come in to make this type of experience as satisfactory as possible, raising the level of understanding between machines and humans. 

It may seem like a science fiction film, but it is already a reality on the market! See how this all works below.

How NLP Works

NLP works by using language techniques, removing everything that could harm the understanding of the message and focusing on what is essential for the person and is executable for the system. 

For example, in the WhatsApp service of a service company, we can imagine the following situation.

When the customer gets in touch and sends a “Hello” message, they usually receive an automatic message back, thanking them for contacting us and providing basic information about the services offered.

Generally, options are suggested with what is most sought after by customers in general. It can be location, opening hours and delivery time.

Suppose the customer responds, “I would like information about the payment method”. In that case, the removal technique will be applied to clean the message and make it executable so that the system can respond to the customer’s demand. 

In the example mentioned, the system eliminates what is not essential in the message (from/about/to…) and focuses on what is executable (inform payment method). 

This is done quickly so the customer receives something like: “Methods of payment: cash, debit and credit card, which can be paid in up to 3 instalments.”

Why So Much Interest In Natural Language?

There is a worldwide movement to optimize the interaction between machines and humans, prioritizing the consumer experience.

This is because it is in the interest of companies to have a system that can respond to as many customers as possible in an automated way.

In addition to less spending on staff for customer service, it generates a more significant investment of time in more strategic positions for the business and the professional.

So much so that this is one of the areas that has developed the most over the years, just notice how platforms with online search resources such as Google and YouTube, among others, deliver results that are increasingly aligned with user expectations.

Data Science professionals, on the other hand, find NLP techniques to be great allies in understanding how to extract data from conversations in an organized and efficient way.

With this in mind, we have separated 4 of the main ways to use Natural Language Processing to extract data and insights! Check out:

4 Natural Language Techniques For Every Data Scientist To Know

Before talking more specifically about the techniques, it is essential to highlight that the field of linguistics offers essential contributions to the various NLP techniques, as it is an area that deals with aspects inherent to human language. Are they:

  • sound of words (phonetics);
  • composition and interpretation of words (morphology and lexicon);
  • composition and interpretation of sentences (syntactic and semantic);
  • discursive analysis (discourse);
  • interpretation of concepts (pragmatic).

Without understanding how this is articulated within the language, it would be impossible to develop the applicability of NLP that we highlight throughout the text. We can also highlight:

  • simultaneous translation;
  • voice command;
  • automatic correctors;
  • GPS;
  • among others.
  • Without further ado, let’s get to NLP techniques!


It is a technique articulated in three topics:

Word composition: grouping different forms of a word; for example, different verbal conjugations (and, and, and…) are grouped in the form of the infinitive (andar);

vocabulary analysis: in association with dictionary databases, groups words with the same meaning for standardization (walking, changes to walking);

Context: differentiates words that can have different meanings depending on their use (mango meaning fruit or sleeve of clothes).

Lemmatization represents a crucial qualitative gain in understanding what the user wants, as it deals with a set of more complex aspects present in natural language. 


It is a more simplified technique than Lemmatization, as there is a single articulation that is the extraction of the radical.

This means that considering the examples above, the different verbal conjugations only result in the extraction of the radical (and).

The Stemization technique represents a complication in analyzing discourse or feeling since it does not differentiate words by context. 

Furthermore, completely different words can receive the same stem extraction, resulting in inconvenient experiences as in our initial example, in which the machine does not understand what we want to express. 

Sentiment Analysis

This is a widespread technique that, without a doubt, represents a challenge for natural language studies, as it deals with the levels of human subjectivity and the attempt to capture the feelings behind the words in the text.

The technique can achieve more straightforward results by classifying feelings as positive, negative or neutral. However, obtaining more complex results with supervised or unsupervised analysis is also possible. 

In the case of supervised analyses, these can be carried out based on probabilistic classifications.

Keyword Extraction, Detection or Analysis

It is a technique centered on automatically extracting keywords, facilitating, for example, social media monitoring, customer service, product analysis and search engine optimization.

Also Read: Artificial Intelligence: 7 Examples From Your Everyday Life

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