Scikit-Learn NLP Tutorials Tutorial

This tutorial will guide you through the basics of Natural Language Processing (NLP) using Scikit-Learn. We'll cover various topics such as text preprocessing, feature extraction, and model training.

Overview

Text Preprocessing: Cleaning and preparing text data for modeling.
Feature Extraction: Transforming text data into numerical features that can be used by machine learning algorithms.
Model Training: Training various NLP models on your dataset.

Text Preprocessing

Text preprocessing is the first step in any NLP task. It involves cleaning the text data and making it suitable for further processing.

Tokenization: Splitting text into words or tokens.
Lowercasing: Converting all characters to lowercase.
Removing Stopwords: Eliminating common words that do not contribute much meaning.
Stemming/Lemmatization: Reducing words to their base or root form.

Example

from sklearn.feature_extraction.text import CountVectorizer

text = "This is a sample text for NLP tutorial."
vectorizer = CountVectorizer()
X = vectorizer.fit_transform([text])
print(X.toarray())

Feature Extraction

Feature extraction is the process of converting text data into a format that can be used by machine learning algorithms.

Bag of Words (BoW): Representing text as the frequency of words.
TF-IDF: Representing text based on the importance of words in the document and across all documents.

Example

from sklearn.feature_extraction.text import TfidfVectorizer

text = "This is a sample text for NLP tutorial."
vectorizer = TfidfVectorizer()
X = vectorizer.fit_transform([text])
print(X.toarray())

Model Training

Once you have your text data in a suitable format, you can train various NLP models on your dataset.

Naive Bayes: A probabilistic classifier based on Bayes' theorem.
Support Vector Machine (SVM): A powerful classifier that works well with high-dimensional data.

Example

from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import train_test_split

text = "This is a sample text for NLP tutorial."
labels = ["positive"]

X_train, X_test, y_train, y_test = train_test_split(text, labels, test_size=0.2)

model = MultinomialNB()
model.fit(X_train, y_train)

print(model.predict(X_test))

Resources

For more information on NLP and Scikit-Learn, check out the following resources: