Scikit-Learn NLP Introduction

Scikit-Learn is a powerful Python library for machine learning that also offers great support for Natural Language Processing (NLP). This tutorial will give you a basic introduction to using Scikit-Learn for NLP tasks.

Basic Concepts

Tokenization: Splitting text into words or sentences.
Stop Words: Common words that are usually removed from text data (e.g., "the", "is", "and").
Vectorization: Converting text data into numerical vectors that can be used for machine learning algorithms.

Key Libraries

NLTK: A leading platform for building Python programs to work with human language data.
SpaCy: An industrial-strength natural language processing library.
TextBlob: A simple library for diving into common natural language processing (NLP) tasks such as part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, translation, and more.

Example: Sentiment Analysis

Let's say we want to classify movie reviews as positive or negative. We can use Scikit-Learn to build a simple model for this task.

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB

# Sample data
reviews = [
    "This movie was amazing!",
    "I did not like this movie at all.",
    "It was okay, nothing special.",
    "What a fantastic movie!",
    "I hate this movie."
]

labels = [1, 0, 0, 1, 0]  # 1 for positive, 0 for negative

# Vectorize the text
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(reviews)

# Split the data into training and test sets
X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.2)

# Train a Naive Bayes classifier
classifier = MultinomialNB()
classifier.fit(X_train, y_train)

# Test the classifier
print(classifier.predict(vectorizer.transform(["This movie was not good."])))

More Resources

For further reading, you can check out the following resources:

想要了解更多关于Scikit-Learn NLP的教程，请访问我们的Scikit-Learn NLP教程页面。