Natural Language Processing shapes our everyday lives without calling attention to itself. 

  • Spam filters – check
  • Optical Character recognition – check
  • Voice recognition – check
  • Grammar check 

You get the point.

NLP applications are present in the majority of data processing operations, especially in those who need analysis and generation of content. In the previous article, we explained what NLP is and how it works. In this article, we will take a closer look at the major business applications of this technology.

Natural Language Processing Applications and Use Cases

1 Text Mining, Document Classification – Research and Analysis / Investigation

A text can be considered an entity of boundless possibilities. This is especially true if you have an idea of what you want to get out of it. 

This is what text mining is all about.

The very purpose of text mining is to explore and extract specific insights hidden behind the walls of text. One of the uses of natural language processing is a wide range of research and investigation purposes.

The insights can be different:

  • Valuable information in the text through semantic search (such as quotations, key facts, et al.)
  • Specific details about a certain subject (figures, states, etc)
  • Mentions of specific things in the texts (name mentions and so on)
  • Structural analysis 

The process looks like this:

  • Information Extraction to get a hold on the unstructured text 
  • Categorization – to classify the contents of the text and the role of its elements.
  • Clustering – to group pieces of content with similar or common elements
  • Visualization to streamline the presentation and perception of the results
  • Summarization – to form a concise presentation of what the text is about and what its features are. 

In one way or another, text mining always turns up. The results of the text mining operation can be utilized in a variety of ways. We will look closer at some of them later in this article. 

For now, let’s look at those that serve a specific business purpose:

  • Topic Modeling – can be used to understand what the text and its elements are about. Can be used to extract viable figures and expressions. 
  • The same approach, combined with named-entity recognition, can be applied for tag optimization. The algorithm rounds up the most important words, such as names of people, organizations, and so on.
  • Intent Analysis – can be used to analyze specific phrases and determine the intentions behind them. For example, it can be used to determine whether a customer is going to buy something, or if he still considering different options. 

One of the most prominent tools for text mining and analysis is Voyant Tools. Built for scientific research purposes, it contains a wide variety of tools that help you extract all sorts of insights out of a document.

2 Data Analysis – Market Research / Business Intelligence

Data Analysis is an essential one of the applications of natural language processing. Like text mining, it can be used to dig deep into the roots of a specific document. You can use the same tools on a broader scale. 

The text mining process can be used for more practical purposes by business intelligence analysts. Such purposes might include gaining business intelligence or performing market research. The thing is – texts, infographics, and images of pieces of news, are essential for further decision making.

The purpose of the scraper is: 

  • to check the sources (or look through with specific keywords) 
  • note the stuff of interest (determined by your business needs).

In essence, this process is data mining but oriented towards text-based data and related paraphernalia. Such applications can visualize and present insights within a couple of clicks with accompanying reports. 

NLP helps to get a hold of this information without a fuss. Classification and categorization algorithms impact decisions. 

As such, NLP can be used for the following purposes:

  • To explore the market situation and coverage of  specific subjects;
  • To study the impact of your actions on the market. 
  • To study the competitor’s behavior. 
  • To explore the customer’s persona, needs, and demands. 
  • To find valuable information hidden in reports or other pieces of content.
  • To be aware of the news about competition events, technologies, and other important events.

The original is here

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