Natural Language Processing with Python: Analyze and Understand Text Using NLP

 

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Part 6: Natural Language Processing (NLP) with Python

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🧠 What Is Natural Language Processing (NLP)?

NLP is a branch of AI that helps computers understand, interpret, and generate human language. It powers:

• Search engines

• Translation apps

• Chatbots

• Voice assistants

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🧰 Tools We'll Use

• NLTK – Natural Language Toolkit

• TextBlob – Simple text analysis

• spaCy – Fast and industrial-strength NLP

Install them with:

pip install nltk textblob spacy
python -m textblob.download_corpora
python -m nltk.downloader punkt

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✍️ Step-by-Step: Basic Text Analysis with TextBlob

✅ Step 1: Create a Simple Analyzer

from textblob import TextBlob

text = "Python is a powerful language for machine learning."
blob = TextBlob(text)

print("Words:", blob.words)
print("Sentences:", blob.sentences)

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💬 Step 2: Sentiment Analysis

text = "I love working with Python, but debugging can be frustrating."
blob = TextBlob(text)
print(blob.sentiment)

Output:

Sentiment(polarity=0.25, subjectivity=0.6)

• Polarity ranges from -1 (negative) to +1 (positive)

• Subjectivity ranges from 0 (objective) to 1 (subjective)

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🔍 Tokenization and Lemmatization with NLTK

import nltk
from nltk.tokenize import word_tokenize
from nltk.stem import WordNetLemmatizer

nltk.download('punkt')
nltk.download('wordnet')

text = "Cats are running faster than the dogs."
tokens = word_tokenize(text)
lemmatizer = WordNetLemmatizer()

lemmas = [lemmatizer.lemmatize(token.lower()) for token in tokens]
print("Lemmatized Tokens:", lemmas)

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🧠 Named Entity Recognition with spaCy

import spacy

nlp = spacy.load("en_core_web_sm")
doc = nlp("Apple is looking at buying a startup in the UK for $1 billion.")

for entity in doc.ents:
    print(entity.text, "-", entity.label_)

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🤖 Mini Project: Simple Sentiment Classifier

def get_sentiment(text):
    blob = TextBlob(text)
    polarity = blob.sentiment.polarity
    if polarity > 0:
        return "Positive"
    elif polarity < 0:
        return "Negative"
    else:
        return "Neutral"

print(get_sentiment("I love AI and machine learning!"))  # Positive
print(get_sentiment("I hate bugs in my code."))          # Negative

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🧪 Practice Challenge

1. Ask the user for input and return sentiment

2. Try building a chatbot that responds based on detected sentiment

3. Use spaCy to extract named entities from a paragraph

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🎓 What You’ve Learned:

• How to tokenize and lemmatize text

• Perform sentiment analysis

• Use NLP libraries like NLTK, TextBlob, and spaCy

• Build a simple sentiment classifier

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🧭 What’s Next?

In Part 7, we’ll tackle Computer Vision using OpenCV and Deep Learning. You’ll learn how to analyze and classify images using Convolutional Neural Networks (CNNs).

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