Natural Language Processing (NLP)

1. What is NLP?

Natural Language Processing (NLP) is the part of AI that deals with understanding and using human language. It connects linguistics (how we speak) and machine learning (how systems learn from text).

2. How NLP Works — Step by Step

1. Text Preprocessing: Clean and split text into tokens (words or subwords).
2. Feature Extraction: Convert words into numbers (embeddings).
3. Model Training: Learn from text patterns — such as context, sentiment, or entities.
4. Prediction / Generation: Use the model to classify, summarize, or generate new text.

3. Simple Code Example (Python)

Here’s a tiny demo of how you can use TextBlob to analyze sentiment of text.

from textblob import TextBlob

# Step 1: Create a TextBlob object
text = "I love learning AI for free with Viswanext!"

# Step 2: Get sentiment
sentiment = TextBlob(text).sentiment

print("Text:", text)
print("Sentiment:", sentiment)
      

4. Common NLP Tasks

• Text Classification: Label text (e.g., spam or not spam).
• Sentiment Analysis: Detect emotions from text.
• Named Entity Recognition (NER): Identify people, places, or organizations.
• Machine Translation: Convert one language to another.
• Question Answering: Find answers in documents.

5. Real-World Example — Chatbot in Healthcare

Hospitals use NLP-powered chatbots to assist patients with scheduling, FAQs, or understanding medical reports. The model first converts the patient’s question into tokens, analyzes intent, then gives an accurate response.

6. Try It Yourself

Write down three sentences and guess their sentiment (positive, neutral, or negative). Then check them online using a free NLP demo like Hugging Face Spaces.

7. What’s Next?

Now that you understand NLP basics, the next chapter — Large Language Models (LLMs) — will reveal how modern AI systems like ChatGPT are built upon these NLP foundations.