PYTHON Tutorial

Natural Language Processing

Natural Language Processing (NLP) is a subfield of artificial intelligence that gives computers the ability to understand and generate human language.

Steps in NLP:

Text Preprocessing:
  • Removing stop words (common words like "the", "is")
  • Converting text to lowercase
  • Removing punctuation
Tokenization:
  • Splitting text into individual tokens (words)
Stemming/Lemmatization:
  • Reducing words to their base or root form
Part-of-Speech Tagging:
  • Identifying the grammatical category of words (noun, verb, etc.)

Practical Applications:

Sentiment Analysis:
  • Identifying the sentiment (positive or negative) of text
Named Entity Recognition:
  • Extracting specific entities (names, places, time) from text

Python Example:

# Simple sentiment analysis using scikit-learn
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.linear_model import LogisticRegression

# Create a corpus of labeled tweets
corpus = [
    ("I love this movie!", 1),
    ("This movie is terrible", 0)
]

# Preprocess and tokenize the corpus
vectorizer = CountVectorizer()
X = vectorizer.fit_transform([text for text, _ in corpus])

# Create a logistic regression model
model = LogisticRegression()
model.fit(X, [label for _, label in corpus])

# Predict the sentiment of a new tweet
new_tweet = "The movie was okay."
new_features = vectorizer.transform([new_tweet])
prediction = model.predict(new_features)
print(prediction) # [1] (positive)

Enhanced Guide for Accessibility:

What is NLP? NLP is like a translator for computers. It helps computers understand the words and language that people use.

How NLP Works: NLP follows these steps:

  • It cleans up the text by removing unnecessary words and symbols.
  • It breaks the text into smaller units called "words".
  • It figures out the meaning of each word and how it relates to other words.

Uses of NLP: NLP is used for many things, such as:

  • Figuring out what people are feeling about something
  • Finding important information in documents
  • Translating languages

Example for Beginners:

Imagine you have a sentence: "I love cats."

NLP will break it down into:

  • Words: I, love, cats
  • Meaning: I have a positive feeling towards cats