Introduction to Natural Language Processing (NLP) in Data Science

Natural Language Processing

Natural Language Processing (NLP) encompasses a variety of techniques designed to enable computers to understand and process human languages. In this post you’ll learn about NLP applications like text classification and sentiment analysis, plus NLP techniques like tokenization and stemming.

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Natural Language Processing (NLP) is a subfield of artificial intelligence (AI) and linguistics that focuses on the interaction between computers and human languages. It encompasses a range of techniques aimed at enabling computers to understand, interpret, and generate human language in a manner that is both meaningful and contextually relevant. 

In data science, NLP plays a pivotal role in extracting insights from vast amounts of textual data. Through techniques such as text classification, sentiment analysis, named entity recognition, and language translation, NLP empowers data scientists to analyze and derive actionable insights from unstructured text data sources such as social media, customer reviews, emails, and news articles. By harnessing the power of NLP, data scientists can uncover patterns, trends, and sentiments within textual data. This enables organizations to make data-driven decisions and enhance various aspects of their operations, from customer service to product development and market analysis.

NLP is fundamental to generative AI models like ChatGPT. Natural language processing techniques enable these models to understand and generate human-like text, making them capable of engaging in meaningful conversations with users. NLP provides the framework for tasks such as language understanding, sentiment analysis, summarization, and language generation. All are essential components of generative AI systems.

Applications of NLP

NLP techniques are extensively utilized in text classification and sentiment analysis, offering a wide array of applications across various industries.

Text Classification

NLP enables automatic categorization of textual data into predefined classes or categories. Applications include:

  • Spam detection: NLP algorithms can classify emails or messages as spam or non-spam, helping users manage their inbox efficiently.
  • Topic classification: NLP models categorize news articles, research papers, or social media posts into relevant topics, aiding in content organization and information retrieval.
  • Language identification: NLP models can identify the language of a given text, which is useful for multilingual platforms and content analysis.

Sentiment Analysis

NLP techniques are employed to analyze the sentiment or emotion expressed in textual data, providing valuable insights for decision-making. Applications include:

  • Brand monitoring: Sentiment analysis helps businesses monitor online conversations about their brand, products, or services. This enable them to gauge public perception and address potential issues promptly.
  • Customer feedback analysis: NLP algorithms analyze customer reviews, surveys, and social media comments. Then, they work to understand customer sentiment towards specific products or services, facilitating product improvement and customer satisfaction.
  • Market research: Sentiment analysis aids in analyzing public opinion and sentiment towards specific topics or events, providing valuable insights for market research, trend analysis, and forecasting.
  • Social media analysis: NLP techniques are utilized to analyze sentiment in social media posts, tweets, and comments, enabling businesses to track customer sentiment, identify influencers, and engage with their audience effectively.

NLP Techniques

NLP encompasses a variety of techniques designed to enable computers to understand and process human languages. Two fundamental techniques in NLP are tokenization and stemming, which play crucial roles in text preprocessing and analysis.


Tokenization is the process of breaking down a piece of text into smaller units (called tokens). These tokens can be words, phrases, or other meaningful elements. The primary goal of tokenization is to divide the text into individual units for further analysis. There are different tokenization strategies, including:

  • Word tokenization divides the text into words or word-like units. For example, the sentence “The quick brown fox jumps over the lazy dog” would be tokenized into [“The”, “quick”, “brown”, “fox”, “jumps”, “over”, “the”, “lazy”, “dog”].
  • Sentence tokenization splits the text into sentences. For instance, the paragraph “Natural Language Processing (NLP) is a fascinating field. It involves analyzing and understanding human language” would be tokenized into [“Natural Language Processing (NLP) is a fascinating field.”, “It involves analyzing and understanding human language.”].


Stemming is the process of reducing words to their root or base form, known as the stem. The goal of stemming is to normalize words so that different forms of the same word are treated as identical. Stemming algorithms apply heuristic rules to remove suffixes and prefixes from words. For example:

  • Original Word: “Running”
  • Stemmed Word: “Run”
  • Original Word: “Jumped”
  • Stemmed Word: “Jump”

Stemming is particularly useful in tasks such as text mining, information retrieval, and search engines. Why? Because stemming reduces words to their base forms can improve indexing and retrieval accuracy. Both tokenization and stemming are essential preprocessing steps in many NLP applications, including text classification, sentiment analysis, machine translation, and information retrieval. These techniques help transform raw textual data into a format suitable for further analysis and modeling, facilitating the extraction of meaningful insights from large volumes of text data.

Natural Language Processing (NLP) Resources

Given the comprehensive overview of NLP’s applications and techniques, several resources can significantly aid in deepening your understanding and skills in this field. Books such as Natural Language Processing in Action by Lane, Howard, and Hapke, and Speech and Language Processing by Jurafsky and Martin provide foundational knowledge and practical examples. These texts are excellent for understanding the underlying principles of NLP. They’re also great for reference on specific topics like tokenization, stemming, and machine learning models used in NLP. Regardless of which NLP resource is used, the key is to practice coding the models.

Learn More about NLP in Flatiron’s Data Science Bootcamp

Flatiron School’s Data Science Bootcamp teaches natural language processing, data analysis and engineering, machine learning fundamentals and much more. Full-time and part-time enrollment opportunities await! Apply today or schedule a call with Admissions to learn more about what Flatiron can do for you and your career. 

Disclaimer: The information in this blog is current as of March 27, 2024. Current policies, offerings, procedures, and programs may differ.

About Brendan Patrick Purdy

Brendan is the senior curriculum developer for data science at the Flatiron School. He holds degrees in mathematics, data science, and philosophy, and enjoys modeling neural networks with the Python library TensorFlow.

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