Imagine the following situation: a huge company — let’s call it Applesoft — wants to track the opinions of its customers. But it sells so many products that it physically can’t go through all the reviews. After all, hiring people to process and analyze customer feedback would be costly. So instead, Applesoft makes its machines do all the work.
The process is now automatic. Computers go through all the text data, brush off irrelevant details, and pick out key points about each product, noting the most frequently used words.
However, before they can process reviews, the computers need to be trained how. This is where data scientists come in, and one of the tools they use to train machines is NLTK, or the Natural Language Toolkit.
In this article, we discuss NLTK and how to master it.
How NLTK relates to data science
In today’s digital age, data has become the fuel that drives every website, application, and software. It enables them to analyze customer behavior, optimize marketing strategies, improve the logistics of supply chains, and so much more.
There’s a professional field focused on using statistical, computational, and analytical methods to process information. It’s called data science. Experts in this field develop ways to extract insights and knowledge from complex data sets in a variety of industries, such as finance, healthcare, marketing, and technology, to name a few.
Companies often apply data science tools to optimize and improve their business processes.
For example, Walmart has been looking for correlations between real-life events and customer shopping patterns to build marketing campaigns off of them. In fact, one study revealed a surprising trend: strawberry Pop-Tarts sell seven times better than usual during the hurricane season. For some reason, people really love stocking up on these sweet treats before a hurricane!
If you are fascinated by data science and would like to learn more, check out this article: Data Science Vs. Data Analytics: Which Path Is Right For You?
Q. What’s the difference between data analytics and data science?
A. Data science is a broader field that encompasses data analytics. Both involve working with data, but data science goes beyond analyzing data sets, and looks at improving machine learning models and developing new tools for data analysts.
So, what exactly is NLTK?
If you've done some reading about data science already, you may have come across the term Python. If not, this is a fairly common programming language that many data scientists use in their everyday work.
But then there are two abbreviations — NLTK and NLP. You’ve already learned that NLTK is a data science tool. So, what’s the second one? Is it any different? (Warning: get ready for some confusing alphabet soup!)
The difference between Python, NLP, and NLTK
Let’s clarify once and for all:
- Python is a programming language used to build applications. It’s widely used in data science, machine learning, and many other areas. It’s known for its simplicity, versatility, and ease of use.
- NLP (Natural Language Processing) is a field of study that focuses on the interaction between computers and human languages. It’s a branch of artificial intelligence that aims to teach machines to understand human speech.
- NLTK (Natural Language Toolkit) is a Python library that provides tools and resources for natural language processing. It’s one of the most widely used NLP libraries in Python. It includes functions and algorithms for various NLP tasks.
Now, let’s dive deeper into NLTK, how it works, and what it is used for.
NLTK is an add-on for Python designed to build NLP tools
Imagine a country — let’s call it the Data Science Republic. Its citizens have their own national language, or languages, since there might be a few. One of them is Python.
The citizens of the republic work in a variety of fields and professions. Sometimes they add new terms to their national language, fine-tuning it to fit their professional fields. That’s how jargon appears.
Jargon is a slightly different version of a language that caters better to a specific group of people.
In our case, NLTK is like jargon for Python, and it’s common for the field of Natural Language Processing. People noticed that Python lacked instruments for typical NLP processes, so they decided to come up with NLTK and adjust it specifically for their needs. In other words, NLTK is a special add-on for Python that provides tools and resources for NLP purposes.
The most useful NLTK features
NLTK provides a comprehensive set of tools for working with text data, which is why companies often develop and implement NLTK-based solutions. To give you a better idea, here are some use cases of this Python library in the business world.
NLTK classifies texts into categories
If you open your Gmail inbox, you’ll see that it has tabs with various categories:
When you receive an email, it gets filtered into one of these tabs. This happens thanks to a process called Text Classification: it’s when algorithms scan the content of texts and put them into predefined categories.
Here’s how it works. NLTK analyzes each word in a text and labels it according to its part of speech (nouns, verbs, adjectives, and adverbs). This tool is called part-of-speech tagging. Machine learning models use it to identify the structure of sentences and understand context.
That’s how NLTK can train computers to recognize patterns in text. These patterns might be associated with different topics (e.g., ecology, sports, or entertainment). This can be useful for organizing large amounts of blog posts, news articles, research papers, or other text documents.
The next feature we’ll discuss also commonly involves part-of-speech tagging.
NLTK can understand opinions expressed in text
Picture this: you buy a Dyson vacuum cleaner, but the purchase turns out to be awful, and you quickly notice the smell of burnt plastic. So, you write a customer review that says: “This product is terrible. I’m so disappointed that I spent my money on it.”
Now, Dyson would surely want to know that you had issues. What if it’s not a single faulty vacuum cleaner, but an entire batch or model line that has the same problem? However, the brand sells thousands of vacuum cleaners each week. Your review would be buried under a pile of other customers’ reviews that say: “The product is great and exceeded my expectations.”
The bigger the company, the harder it is to read customer feedback. So, data scientists came up with a technique called Sentiment Analysis. It’s when a computer determines whether a particular piece of text expresses a positive, negative, or neutral opinion.
Sentiment analysis would identify your review as negative even if you forgot to give it one star. And vice versa — if you were satisfied with the purchase, your review would be labeled as positive.
Apart from feedback monitoring, NLTK can also perform sentiment analysis on various types of text data:
- Social media posts
- Emails and messages
- News articles and so on
Businesses can use sentiment analysis to track brand reputation on social media. When applied to healthcare, politics, sociology, and other fields, it helps analyze public opinion on various topics.
This NLTK technique is based on algorithms that can analyze text and identify key words and phrases. Some models may also take into account context, tone, and writing style to determine the sentiment more accurately.
Businesses employ NLTK for market analysis
Have you ever noticed this? There’s always some sort of tit-for-tat with competing brands. Any time McDonald’s launches a new campaign (for example, “Buy one burger, get one free”), Burger King follows with its own announcement.
However, brands can’t simply improvise and hope for the best. So, before starting its own special offer, Burger King needs to determine the best way to respond. One way could be to apply NLTK-based market analysis. For instance, Burger King could track customers’ opinions on social media and figure out how to win over McDonald’s lovers.
With the rise of big data, companies turn to natural language processing to extract valuable market insights from unstructured sources, such as:
- Social media activity
- Customer comments
- Press releases
- News reports
Using NLTK-trained software, businesses can get a sense of customer preferences, opinions, and pain points. It is also a common method of keeping track of current market trends and competitors’ actions. This way, whenever changes occur in the industry, brands are prepared to adjust to them.
NLTK recognizes people, organizations, and locations in text
Movies make it seem like working for a media outlet is extremely chaotic: smartly-dressed people running around, piles of papers thrown in the air, and the constant ringing of dozens of landline phones. (How else would the newspaper get the scoop on the hottest trends, right?)
Thanks to today's technologies, it’s much easier (and quieter). Media can simply use a program that tracks the number of mentions of some celebrity, place, or organization. To do this, they feed a collection of news articles to a piece of software. This allows the media to understand what personalities and businesses are currently trending.
Data scientists are also tasked with coding these kinds of programs. With NLTK, they can extract proper nouns from text data, such as:
- John Smith
- New York City
Basically, NLTK teaches computers to identify and classify named entities in text. This process is called Named Entity Recognition. It also helps with information extraction, text summarization, and postal address parsing.
NLTK can help companies build chatbots
Think of the last time you browsed a company’s website. Did you notice the annoying pop-ups in the bottom-right corner of your screen? If you click one, a tab will open where you can consult with an assistant. More often than not, instead of actual human specialists, you are talking to robots.
These virtual assistants are called chatbots, and they are also based on NLTK. Their task is to understand natural language input and simulate conversation with human users. Companies use chatbots to automate customer service and support.
Chatbots can handle a wide range of customer inquiries, including:
- Product information
- Order status
- Refunds & returns
Using machine learning algorithms, chatbots can be trained to improve their performance over time. This way, they will provide accurate and relevant responses to users.
NLTK tips for beginners
There are two ways to get into NLTK: self-education and bootcamps.
We’ll start with the first one. Here are some recommendations for those who want to master NLTK on their own:
- Familiarize yourself with Python: This is the programming language NLTK is written in, so it’s important to have a basic understanding of it. You should be comfortable with Python syntax, data types, and control flow statements.
- Learn more about natural language processing: Before diving into NLTK, it’s useful to acquire at least a minimal knowledge of NLP. Read about pre-processing steps and get comfortable with terms like corpus, tokenization, and part-of-speech tagging.
- Browse NLTK.org: This is the official website for the Natural Language Toolkit. Here you will find information on NLTK and its features, documentation, installation instructions, and a list of supported datasets.
- Become a part of the NLTK community: There are tons of NLTK users and developers who are happy to help newcomers. Join online forums or groups to connect with others and learn from their experiences.
- Practice, practice, practice: Like any skill, it takes time to master natural language processing. Regular training will build your skills and deepen your understanding of the NLTK library.
If this roadmap seems too overwhelming, consider signing up for TripleTens Data Science Bootcamp. With our innovative platform and expert instruction, you’ll acquire the full set of skills and knowledge essential for any data scientist — NLTK included. This will be more than enough to land your first job in the industry.
NLTK (Natural Language Toolkit) is a Python library that provides tools and resources for natural language processing. It’s one of the most widely used Python libraries for dealing with NLP tasks.
NLTK is a common instrument in data science — a broad field that develops methods of working with data sets.
Companies use this library for a variety of purposes:
- Running sentiment analysis on customer reviews, social media posts, etc.
- Automatic categorization of text pieces (e.g., “spam/not spam” in emails)
- Tracking current trends and competitors’ developments via market analysis
- Programming chatbots or virtual assistants that consult website visitors
To grow from an NLTK novice to an expert, we recommend diving deeper into Python and NLP. Follow that up by joining the friendly community of NLTK enthusiasts and continuing to develop your skills.
Good luck on your awesome journey!