Text Classification

🎯 NLP Applications: Text Classification

The Magical Sorting Hat for Words

Imagine you have a magical sorting hat (like in Harry Potter!) but instead of sorting students into houses, it sorts text into categories. That’s exactly what Text Classification does in the world of AI!

🏷️ What is Text Classification?

Think of it like sorting your toys into different boxes:

• 🧸 Stuffed animals go in one box
• 🚗 Cars go in another box
• 🧩 Puzzles go in a third box

Text Classification does the same thing but with words and sentences!

Real-Life Examples:

graph TD
    A["📝 Text Input"] --> B{🎩 Classifier}
    B --> C["📁 Category 1"]
    B --> D["📁 Category 2"]
    B --> E["📁 Category 3"]

How Does It Work?

1. Show examples - Give the AI many texts with their correct labels
2. AI learns patterns - It figures out what makes each category special
3. Sort new text - Now it can classify text it never saw before!

Simple Example:

• “I love pizza!” → Food topic ✅
• “The goal was amazing!” → Sports topic ✅
• “New phone released” → Technology topic ✅

😊😢😡 Sentiment Analysis

Reading Emotions in Text

Ever wonder how Netflix knows if people like a movie? Or how companies know if customers are happy?

Sentiment Analysis is like teaching AI to read emotions!

The Three Emotion Buckets:

graph TD
    A["📝 Review Text"] --> B{🧠 Sentiment Analyzer}
    B --> C["😊 Positive"]
    B --> D["😐 Neutral"]
    B --> E["😢 Negative"]

Real Examples:

Why Is It Useful?

• Businesses know if customers are happy
• Movie studios see if people like their films
• Politicians understand how voters feel
• You can find the best products by checking reviews!

The Magic Behind It:

The AI looks for signal words:

Positive signals: love, amazing, fantastic, great, happy Negative signals: hate, terrible, awful, bad, disappointed

But it’s smarter than just counting words! It understands:

• “Not bad” = Actually positive!
• “Could be better” = Actually negative!

🏷️ Named Entity Recognition (NER)

Highlighting the Important Stuff

Imagine you’re reading a story and using a highlighter to mark:

• 🟡 Yellow for people’s names
• 🟢 Green for places
• 🔵 Blue for companies
• 🟣 Purple for dates

That’s exactly what Named Entity Recognition does!

What Are “Named Entities”?

Special words that have specific meanings:

graph TD
    A["Named Entities"] --> B["👤 PERSON<br>Elon Musk, Emma Watson"]
    A --> C["📍 LOCATION<br>Paris, Mount Everest"]
    A --> D["🏢 ORGANIZATION<br>Google, NASA"]
    A --> E["📅 DATE/TIME<br>Monday, 2024"]
    A --> F["💰 MONEY<br>$500, €100"]

Real Example:

Input Text:

“On December 25th, Apple announced that Tim Cook would visit Tokyo to meet with Sony executives.”

After NER:

“On [December 25th]DATE, [Apple]ORG announced that [Tim Cook]PERSON would visit [Tokyo]LOCATION to meet with [Sony]ORG executives.”

Why Is This Super Useful?

How Does AI Learn This?

The AI notices patterns like:

• Capital letters often mean names or places
• Words after “Mr.” or “Dr.” are usually names
• Numbers with “$” are money
• Words before “Inc.” or “Corp.” are companies

🔗 Sequence Labeling

Every Word Gets a Tag!

Remember playing “Duck, Duck, Goose”? Each kid gets labeled as “Duck” or “Goose.”

Sequence Labeling is similar - every single word in a sentence gets its own special label!

The Big Difference:

Most Famous Example: Part-of-Speech Tagging

Every word gets labeled with its grammar role:

Sentence: “The quick brown fox jumps”

graph LR
    A["The<br>DET"] --> B["quick<br>ADJ"]
    B --> C["brown<br>ADJ"]
    C --> D["fox<br>NOUN"]
    D --> E["jumps<br>VERB"]

Another Example: BIO Tagging for NER

When finding names and places, we use special tags:

• B = Beginning of entity
• I = Inside/continuation of entity
• O = Outside (not part of any entity)

Sentence: “New York is beautiful”

Why Sequence Labeling Matters:

1. Grammar checkers - Know if you used the right word type
2. Translation apps - Understand sentence structure
3. Voice assistants - Parse exactly what you said
4. Search engines - Know the role of each word in your query

🎯 How They All Connect

These four techniques are like a team working together:

graph TD
    A["📝 Raw Text"] --> B["Text Classification<br>What category?"]
    A --> C["Sentiment Analysis<br>What emotion?"]
    A --> D["Named Entity Recognition<br>Who/What/Where?"]
    A --> E["Sequence Labeling<br>Grammar of each word"]

    B --> F["🎯 Complete<br>Understanding"]
    C --> F
    D --> F
    E --> F

Real World Example - Processing a Tweet:

Tweet: “Just visited Apple Store in NYC - amazing experience! 😍”

🌟 Key Takeaways

1. Text Classification = Sorting text into boxes (like spam detection)

2. Sentiment Analysis = Reading emotions in text (positive, negative, neutral)

3. Named Entity Recognition = Highlighting important names, places, organizations

4. Sequence Labeling = Tagging every single word with its role

Together, these tools help computers truly understand human language - not just read it, but UNDERSTAND it!

That’s the magic of NLP! ✨