Text Classification

• Introduction to text classification

• Supervised and unsupervised classification techniques

• Comparison of classification algorithms (Naive Bayes, SVM, etc.)

• Evaluation metrics for text classification (accuracy, precision, recall, F1-score)

• Hands-on exercise: Building a text classification model using a preprocessed and feature-extracted dataset in Python

Introduction to Text Classification

Text classification is the process of categorizing text into predefined groups or classes based on its content. It's an essential task in Natural Language Processing (NLP) and has numerous applications, such as spam detection, sentiment analysis, and topic labeling.

Supervised and Unsupervised Classification Techniques

🔍 Supervised classification refers to the process of training a model with labeled data - texts that have been pre-assigned to specific categories. The model then uses this information to classify new, unlabeled text based on learned patterns.

🔎 Unsupervised classification involves clustering and organizing text data without any prior labeling. The model identifies patterns or relationships within the text and groups similar ones together, allowing the user to explore and interpret the resulting clusters.

Comparison of Classification Algorithms

Naive Bayes

✅ Naive Bayes is a simple and efficient algorithm based on Bayes' theorem. It assumes that the features (words in this case) are independent, making it easier to compute the probabilities. This algorithm is often used as a baseline and performs surprisingly well for text classification tasks.

from sklearn.naive_bayes import MultinomialNB
classifier = MultinomialNB()
classifier.fit(X_train, y_train)

Support Vector Machines (SVM)

✅ Support Vector Machines is another popular algorithm for text classification. It works by finding a hyperplane that best separates the classes in the feature space. The algorithm is effective for high-dimensional data and typically yields good performance.

from sklearn.svm import SVC
classifier = SVC(kernel='linear')
classifier.fit(X_train, y_train)

Evaluation Metrics for Text Classification

Accuracy

🎯 Accuracy is the ratio of correctly classified instances to the total number of instances. It's a widely used metric but can be misleading if the data is imbalanced.

from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, y_pred)

Precision, Recall, and F1-Score

🎯 Precision is the ratio of true positive predictions to the total number of positive predictions. It indicates how well the model correctly classifies positive instances.

🎯 Recall is the ratio of true positive predictions to the total number of actual positive instances. It shows how well the model identifies positive instances.

🎯 F1-score is the harmonic mean of precision and recall, providing a single metric that balances both. It's useful when there's an uneven class distribution or when false positives and false negatives carry different costs.

from sklearn.metrics import classification_report
report = classification_report(y_test, y_pred)

Hands-on Exercise: Building a Text Classification Model

In this exercise, you'll build a text classification model using a preprocessed and feature-extracted dataset in Python.

Step 1: Prepare the Data

First, import the necessary libraries and load the data:

import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.feature_extraction.text import TfidfVectorizer

data = pd.read_csv('preprocessed_data.csv')

Step 2: Split the Data into Training and Testing Sets

Split the data into training (80%) and testing (20%) sets:

X_train, X_test, y_train, y_test = train_test_split(data['text'], data['label'], test_size=0.2, random_state=42)

Step 3: Extract Features Using TF-IDF

Use the TfidfVectorizer to extract features from the preprocessed text:

vectorizer = TfidfVectorizer()
X_train = vectorizer.fit_transform(X_train)
X_test = vectorizer.transform(X_test)

Step 4: Train and Evaluate the Classifier

Train a Naive Bayes classifier and then evaluate its performance:

from sklearn.naive_bayes import MultinomialNB
from sklearn.metrics import classification_report

classifier = MultinomialNB()
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)

report = classification_report(y_test, y_pred)
print(report)

You've now successfully built a text classification model using a preprocessed and feature-extracted dataset in Python!