Add SVM-based sentiment analysis: source classification.

code/svm_source_classifier.py

@@ -0,0 +1,95 @@
+# -*- coding: utf-8 -*-
+"""
+SVM_source_classifier.py
+This code trains a SVM model to classify the source of a text document
+based on writing style and topic using the fetch_20newsgroups dataset.
+Removes headers, footers, and quotes to prevent data leakage.
+Author: Jane Heng
+Date: Oct.10.2025
+"""
+from sklearn.datasets import fetch_20newsgroups
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.svm import SVC
+from sklearn.pipeline import make_pipeline
+from sklearn import metrics
+
+def load_data(categories):
+    """
+    Loads training and testing data from the 20 Newsgroups dataset for the specified categories.
+    Removes headers, footers, and quotes to reduce noise and prevent data leakage.
+
+    Args:
+        categories (list of str): List of category names to include.
+
+    Returns:
+        tuple: A tuple containing:
+            - train_data (list of str): Training text samples.
+            - train_target (list of int): Labels for training samples.
+            - test_data (list of str): Testing text samples.
+            - test_target (list of int): Labels for testing samples.
+            - target_names (list of str): Names of the target categories.
+    """
+    train_data = fetch_20newsgroups(
+        subset='train',
+        categories=categories,
+        remove=('headers', 'footers', 'quotes')
+    )
+    test_data = fetch_20newsgroups(
+        subset='test',
+        categories=categories,
+        remove=('headers', 'footers', 'quotes')
+    )
+    return train_data.data, train_data.target, test_data.data, test_data.target, train_data.target_names
+
+def train_model(X_train, y_train):
+    """
+    Trains a Support Vector Machine (SVM) classifier using TF-IDF features.
+
+    Args:
+        X_train (list of str): Training text data.
+        y_train (list of int): Corresponding labels for training data.
+
+    Returns:
+        sklearn.pipeline.Pipeline: A trained pipeline combining TF-IDF vectorization and SVM classification.
+    """
+    model = make_pipeline(TfidfVectorizer(), SVC(kernel='linear'))
+    model.fit(X_train, y_train)
+    return model
+
+def evaluate_model(model, X_test, y_test, target_names):
+    """
+    Evaluates the trained model on the test data and prints a classification report.
+
+    Args:
+        model (Pipeline): Trained model pipeline.
+        X_test (list of str): Test text data.
+        y_test (list of int): True labels for test data.
+        target_names (list of str): Names of the target categories.
+
+    Returns:
+        None
+    """
+    predicted = model.predict(X_test)
+    print(metrics.classification_report(y_test, predicted, target_names=target_names))
+
+def run_source_classification(categories):
+    """
+    Executes the full classification pipeline:
+    - Loads data for the specified categories
+    - Trains an SVM model
+    - Evaluates model performance
+
+    Args:
+        categories (list of str): List of category names to classify.
+
+    Returns:
+        sklearn.pipeline.Pipeline: The trained model pipeline.
+    """
+    X_train, y_train, X_test, y_test, target_names = load_data(categories)
+    model = train_model(X_train, y_train)
+    evaluate_model(model, X_test, y_test, target_names)
+    return model
+
+if __name__ == "__main__":
+    categories = ['talk.politics.misc', 'rec.sport.hockey', 'sci.space']
+    run_source_classification(categories)

code/test_source_classifier.py

@@ -0,0 +1,71 @@
+# -*- coding: utf-8 -*-
+
+"""
+test_source_classifier.py
+Unit test for svm_source_classifier.py
+Author: Jane Heng
+Date: Oct.10.2025
+"""
+import unittest
+from svm_source_classifier import load_data, train_model, evaluate_model, run_source_classification
+from unittest.mock import patch
+import io
+
+class TestSourceClassifier(unittest.TestCase):
+    """
+    Unit test suite for verifying the functionality of the SVM source classifier pipeline.
+    """
+
+    def setUp(self):
+        """
+        Initializes sample input data and category labels.
+        """
+        self.categories = ['sci.space', 'rec.sport.hockey', 'talk.politics.misc']
+        self.X_sample = [
+            "NASA launched a new satellite.",
+            "The hockey team won the championship.",
+            "Political debates are heating up."
+        ]
+        self.y_sample = [0, 1, 2]
+
+    def test_load_data(self):
+        """
+        Tests whether load_data correctly loads training and testing data.
+        Check if data lengths match and category names.
+        """
+        X_train, y_train, X_test, y_test, target_names = load_data(self.categories)
+        self.assertEqual(len(X_train), len(y_train))
+        self.assertEqual(len(X_test), len(y_test))
+        self.assertEqual(len(target_names), len(self.categories))
+        self.assertIn('sci.space', target_names)
+        print("test_load_data: PASS")
+
+    def test_train_model(self):
+        """
+        Tests if the model is properly trained on sample input.
+        """
+        model = train_model(self.X_sample, self.y_sample)
+        self.assertTrue(hasattr(model, "predict"))
+        print("test_train_model: PASS")
+
+    def test_evaluate_model(self):
+        """
+        Tests whether evaluate_model function.
+        """
+        model = train_model(self.X_sample, self.y_sample)
+        with patch('sys.stdout', new=io.StringIO()) as fake_out:
+            evaluate_model(model, self.X_sample, self.y_sample, self.categories)
+            output = fake_out.getvalue()
+            self.assertIn('sci.space', output)
+        print("test_evaluate_model: PASS")
+
+    def test_run_source_classification(self):
+        """
+        Tests whether run_source_classification function.
+        """
+        model = run_source_classification(self.categories)
+        self.assertTrue(hasattr(model, "predict"))
+        print("test_run_source_classification: PASS")
+
+if __name__ == "__main__":
+    unittest.main()