Lab – Sentiment Classification with SVM

Lab – Sentiment Classification with SVM#

⏳ Loading Pyodide…

Welcome to the SVM Sentiment Showdown! 🎭

You’re about to use Support Vector Machines to read people’s moods — a valuable skill for both business analytics and avoiding awkward meetings. 😅

🧠 Goal#

You’ll train an SVM model to classify customer reviews as positive 😊 or negative 😡.

By the end of this lab, you’ll:

  • Clean and vectorize text data 🧹

  • Train linear and kernel SVMs ⚙️

  • Evaluate accuracy, precision, recall 📊

  • Visualize misclassifications 👀

  • Understand why SVMs rock for text classification

💼 Business Context#

Imagine you’re the Data Scientist at a coffee chain ☕. The marketing team wants to monitor customer feedback from social media and reviews.

They ask:

“Can we automatically detect unhappy customers before they go viral on Twitter?” 😬

Your answer:

“Hold my latte. I’ll train an SVM.” ☕🤓

🧰 Setup#

Let’s grab the tools first.

`

📥 Step 1: Load the Data#

If you don’t have a dataset, here’s a quick synthetic one for practice:

🧹 Step 2: Split and Vectorize#

Convert text into numbers using TF–IDF (because SVMs can’t read English, only math).

⚙️ Step 3: Train the SVM Model#

We’ll start simple — a Linear SVM.

📊 Step 4: Evaluate Performance#

Let’s see how well it understands human emotions.

Sample output:

              precision    recall  f1-score   support

    negative       1.00      1.00      1.00         1
    positive       1.00      1.00      1.00         1

    accuracy                           1.00         2
   macro avg       1.00      1.00      1.00         2
weighted avg       1.00      1.00      1.00         2

SVM: “I can sense your mood with 100% confidence.” 😎 (Just don’t show it internet sarcasm yet.)

📉 Step 5: Visualize Confusion Matrix#

🔬 Step 6: Experiment with Kernels#

Let’s try a non-linear kernel — maybe the data has emotional complexity. 💅

You’ll likely get similar performance for small text datasets — but on real data (like thousands of tweets), kernel SVMs can reveal deeper sentiment patterns. 💬

🧠 Optional Challenge: Use a Real Dataset#

Try with:

Then:

  1. Clean the text (re, nltk, or spaCy)

  2. Vectorize (TfidfVectorizer)

  3. Train multiple SVMs with different kernels

  4. Compare performance

  5. Make a dashboard that highlights “Top 10 Angry Words” 😤

💼 Business Insight#

Sentiment analysis isn’t just for fun — it’s used in:

  • Customer experience tracking

  • Brand reputation monitoring

  • Product feedback prioritization

  • Stock market sentiment prediction 📈

With SVMs, you can scale these insights across thousands of reviews and alert management before the next PR crisis hits. 🚨

💬 TL;DR#

Step

What You Did

Why It’s Cool

1

Loaded text data

Coffee reviews are data too ☕

2

Vectorized using TF–IDF

Turned words into math

3

Trained Linear SVM

Found the “mood boundary”

4

Evaluated results

Quantified emotions

5

Visualized confusion matrix

Feelings meet charts

6

Tried kernels

Got fancy and flexible

💡 SVMs may not have feelings, but they’re really good at detecting yours. 💔🤖❤️

🔗 Next Chapter: Ensemble Methods & Tree-Based Models Because sometimes it takes a forest 🌲 to make the right decision.

# Your code here
Contents