Python Program for Text Classification of a Given Sentence Using NLTK

Introduction

Text classification is a common task in Natural Language Processing (NLP) where we assign categories to text. For example, classifying a sentence as positive or negative sentiment, spam or not spam, etc. In this article, you’ll learn how to build a simple text classification program in Python using the NLTK library.

What is Text Classification?

Text classification is the process of categorizing text into predefined labels. It is widely used in:

• Sentiment analysis
• Spam detection
• Topic labeling
• Chatbots and recommendation systems

Approach to Build a Text Classifier
We will create a simple classifier using NLTK. The steps include:

• Preparing a small dataset
• Converting text into features
• Training a Naive Bayes classifier
• Testing it on a new sentence

Requirements
Install NLTK if not already installed:

pip install nltk

Download required datasets:

import nltk
nltk.download('punkt')

Python Program for Text Classification

import nltk
from nltk.tokenize import word_tokenize
from nltk.classify import NaiveBayesClassifier

# Sample dataset (sentence, label)
data = [
    ("I love this product", "positive"),
    ("This is amazing", "positive"),
    ("I feel great today", "positive"),
    ("I hate this", "negative"),
    ("This is terrible", "negative"),
    ("I feel bad", "negative")
]

# Feature extraction function
def extract_features(sentence):
    words = word_tokenize(sentence.lower())
    return {word: True for word in words}

# Prepare training data
training_data = [(extract_features(text), label) for (text, label) in data]

# Train classifier
classifier = NaiveBayesClassifier.train(training_data)

# Test sentence
test_sentence = "I love this"
features = extract_features(test_sentence)

# Predict
result = classifier.classify(features)

print("Sentence:", test_sentence)
print("Predicted Sentiment:", result)

Output

Sentence: I love this
Predicted Sentiment: positive
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Explanation of the Code

• A small dataset is created with labeled sentences
• Each sentence is converted into features (words as keys)
• Naive Bayes classifier is trained using this data
• A new sentence is tested and classified

How to Improve This Model

• Use a larger dataset
• Remove stop words for better accuracy
• Apply stemming or lemmatization
• Use advanced models like logistic regression or deep learning

Why Text Classification is Important

• Helps in analyzing large text data
• Useful in social media monitoring
• Improves user experience in applications
• Automates decision-making processes

Conclusion

Text classification using NLTK is a great starting point for beginners in NLP. With just a few lines of code, you can build a basic model that categorizes sentences. As you progress, you can enhance it with more data and advanced techniques.