Wondering about NLP? Start with this beginner-friendly introduction to Natural Language Processing (NLP), explaining how computers understand and work with human language.
Natural Language Processing (NLP) is how computers learn to understand human language. Instead of only working with numbers and code, NLP lets computers read, understand, and even write text like humans do.
NLP in Simple Terms
Think of NLP as teaching a computer to be a really good translator—not just between different languages, but between human language and computer language.
What humans do naturally:
- Read and understand emails
- Know when someone is happy or angry from their words
- Summarize long documents
- Answer questions about what we read
What NLP teaches computers to do: The exact same things, but with thousands of documents in seconds instead of hours.
How You Use NLP Every Day
You're already using NLP without realizing it:
- Voice assistants - Siri, Alexa, Google Assistant understand what you say
- Email spam filters - They read your emails to detect spam
- Google Translate - Converts text between languages
- Chatbots - Customer service bots that answer your questions
- Auto-complete - Your phone suggests what to type next
- Social media - Platforms detect harmful content automatically
What NLP Can Do for Your Business
Analyze Customer Feedback
Instead of reading thousands of reviews manually, NLP can quickly tell you if customers are happy or unhappy and why.
Automate Customer Service
Chatbots can answer common questions 24/7, freeing up human agents for complex issues.
Process Documents Fast
Extract key information from contracts, invoices, or reports in seconds instead of hours.
Monitor Social Media
Track what people are saying about your brand across the internet automatically.
Summarize Long Reports
Turn 50-page reports into 2-page summaries that highlight the most important points.
Common NLP Tasks Made Simple
Sentiment Analysis
Figuring out if text is positive, negative, or neutral. Like reading a restaurant review and knowing if the customer liked the food or not.
Text Classification
Sorting text into categories. Like automatically filing emails into folders: "complaints," "compliments," "questions."
Named Entity Recognition
Finding important names, dates, and places in text. Like automatically highlighting all company names and dates in a contract.
Text Summarization
Creating short summaries of long documents. Like turning a 10-page report into a 3-bullet summary.
Question Answering
Reading documents and answering questions about them. Like asking "What was our revenue last quarter?" and getting the answer from financial reports.
Real Business Examples
Retail Company:
Uses NLP to read customer reviews and automatically categorize complaints by topic (shipping, quality, price) so they can fix the most common problems first.
Bank:
Uses NLP to read loan applications and extract key information (income, employment, debt) automatically instead of having humans type it in.
Hospital:
Uses NLP to read doctor notes and automatically code medical procedures for billing, saving hours of manual work.
Insurance Company:
Uses NLP to read claim descriptions and automatically route them to the right department based on the type of claim.
Why NLP Matters for Data People
Most business data isn't numbers—it's text:
- Customer emails and chat logs
- Survey responses and reviews
- Social media posts and comments
- Support tickets and bug reports
- Contracts and legal documents
NLP turns all this text into useful data you can analyze, just like you would analyze sales numbers or website traffic.
Getting Started with NLP
Step 1: Identify Your Text Data
Look for text data your company already has—customer feedback, support tickets, surveys, social media mentions.
Step 2: Start Simple
Begin with basic tasks like sentiment analysis on customer reviews or automatically categorizing support tickets.
Step 3: Use Existing Tools
Many platforms offer NLP features without requiring programming—Microsoft Power BI, Google Cloud, Amazon AWS all have simple NLP tools.
Step 4: Measure Results
Track how much time NLP saves and how it improves your understanding of text data.
Common Challenges (And Solutions)
Challenge: Text data is messy—typos, slang, abbreviations
Solution: Start with clean, formal text like surveys before tackling social media
Challenge: Computers don't understand context like humans
Solution: Review NLP results and fine-tune for your specific business context
Challenge: Different industries use different language
Solution: Use NLP tools trained on your industry's language (medical, legal, financial)
The TDWI Bottom Line
Natural Language Processing is simply teaching computers to work with text the way they work with numbers. It's not magic—it's a practical tool that can help you analyze customer feedback, automate document processing, and turn text data into business insights.
Start small with one clear use case, like analyzing customer reviews or categorizing support tickets. Once you see the value, you can expand to more complex text analysis projects.
Ready to put text data to work? Explore TDWI's NLP training courses that teach practical text analysis skills with real business examples and hands-on exercises.