Building a Basic Text Classification Model Using Orange

 In the world of Natural Language Processing (NLP), text classification is a foundational task. Whether it's categorizing emails as spam or not, analyzing customer sentiment, or classifying news articles — it's all about teaching machines to understand and categorize text data.

But what if you're not a coder? Or you're teaching undergraduates who are new to machine learning?

That’s where Orange Data Mining comes in — a drag-and-drop, beginner-friendly tool that makes data science and NLP visual and intuitive.

In this blog, we’ll walk you through how to build a basic text classification model in Orange using a real-world case study — all without writing a single line of code.

Feature	Benefit
No-code	Ideal for beginners and educators
Visual workflow	Easy to understand and debug
Extensible	Add-ons for NLP, bioinformatics, text mining
Quick results	Great for rapid prototyping

What You'll Learn

• How to install and set up Orange for text mining

• How to preprocess and vectorize text data

• How to train a machine learning model for classification

• How to evaluate the model using accuracy, confusion matrix, etc.

• Case Study: Classifying movie reviews as Positive or Negative

What You Need

1. Orange Data Mining (free): https://orangedatamining.com/download

2. Text Mining Add-on (we'll show you how to install)

3. A simple CSV dataset with labeled text (provided below)

Case Study: Sentiment Analysis on Movie Reviews

Imagine you're running a movie review website. You want to analyze user reviews and classify them as Positive or Negative.

Here’s a sample of your dataset:

Text	Label
"I absolutely loved the movie!"	Positive
"Worst film I've seen this year."	Negative
"A masterpiece of storytelling."	Positive
"Totally boring and too slow."	Negative

You have hundreds of such reviews — and want a tool to auto-classify them.
Let’s build a model in Orange to do just that.

Step 1: Install Orange and Add-ons

1. Download Orange: https://orangedatamining.com/download

2. Open Orange → Go to Options → Add-ons

3. Check ✅ the Text Mining add-on → Click Install

4. Restart Orange

Step 2: Load Your Data

1. Open Orange Canvas

2. Drag a File widget

3. Load your CSV file (e.g., movie_reviews.csv)
   Make sure your file has:

   • One column named text (the review)

   • One column named label (Positive or Negative)

Tip: Orange auto-detects the label column if it's categorical.

Step 3: Preprocess the Text

1. Drag Preprocess Text widget

2. Connect it to the File widget

3. Double-click it and configure:

   • Lowercase: ✅

   • Remove stopwords: ✅

   • Lemmatization: ✅

   • Tokenization: ✅

This ensures the text is cleaned and normalized for better model performance.

Step 4: Vectorize the Text

1. Add a TF-IDF widget (or Bag of Words for simpler models)

2. Connect it to Preprocess Text

TF-IDF converts the words into numerical features based on how important they are across the dataset.

Step 5: Train the Model

1. Add a Naive Bayes widget (or Logistic Regression, Random Forest)

2. Connect it to TF-IDF

This creates your text classification model using the selected algorithm.

Step 6: Evaluate the Model

1. Add a Test & Score widget

2. Connect both the TF-IDF and the learner (Naive Bayes) widgets

3. Run the evaluation to see:

• Accuracy

• Precision

• Recall

• F1 Score

Step 7: Visualize the Results

You can now add:

• Confusion Matrix → See true vs. predicted labels

• ROC Analysis → See model sensitivity

• Word Cloud → Visualize most common tokens

Output: Your First NLP Classifier

Congrats! You now have a working sentiment classifier that can analyze new movie reviews and predict whether they're Positive or Negative.

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