You can still find the first and most basic application of natural language processing (NLP) in email. Spam filters use NLP to uncover certain words or phrases and signal a spam message. Today, smart assistants like Amazon’s Alexa and Apple’s Siri use it to recognize speech patterns. Search engines surface results based on behavior or intent. This data science development opens a new chapter for how to gain insights: human language. That means businesses can harness previously unusable, unstructured data to rewrite what is possible.

What is Natural Language Processing

Essentially, NLP counts the times certain words repeat in text. It then makes predictions based on the recurrence rates. This allows businesses to break enormous text into sentences. NLP can remove recurring parts of speech like prepositions, conjunctions, and articles, and count the occurrence of each remaining word. What is left is numerical data that can be used to categorize text, determine sentiment, or identify people, organizations, places, and dates. The following image shows a simplified architecture of NLP:

Natural Language Processing

Text Classification

One of the most common applications of NLP in data science is text classification. It involves assigning a label or category to a given piece of text. This function is used to automatically sort and organize copious amounts of text data, such as emails, social media posts, or customer reviews.

As an example, business data scientists at eCapital Advisors trained text classification algorithms to identify and classify the exclusivity of a restaurant’s competitors using text from their menus. The model then used those classifications to generate predictions.

Sentiment Analysis

Another application of NLP in data science is sentiment analysis. This analysis involves using NLP algorithms to automatically identify and extract the emotional content of a piece of text. Then the algorithms can gauge the overall sentiment of a part of the text or identify specific emotions such as joy, anger, or fear. Sentiment analysis is commonly used in customer service. There, it can help companies understand how customers feel about their products or services.

Named Entity Recognition

Named entity recognition (NER) is another landmark capability of NLP in data science. NER automatically identifies and extracts named entities from a text, such as people, organizations, locations, and dates. Businesses can use named entities to organize and structure enormous amounts of text data and to extract valuable information. An example is who is involved in a particular event or where and when it took place.

Natural language processing is a powerful tool in data science that enables computers to understand, interpret, and generate human language. It is used to gain valuable insights from unstructured text data. For more information on how eCapital Advisors can help you rewrite what you can do with unstructured text, contact our business data scientists.