Advanced NLP Tutorial

Welcome to the Advanced NLP Tutorial! This guide will take you through the intricacies of Natural Language Processing (NLP), covering topics such as sentiment analysis, text classification, and language modeling.

What is NLP?

Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the interaction between computers and humans through natural language. This field has seen significant advancements in recent years, enabling computers to understand, interpret, and generate human language.

Key Concepts

Here are some key concepts you should be familiar with before diving into advanced NLP:

Tokenization: The process of breaking text into individual words or tokens.
Part-of-Speech Tagging: Identifying the parts of speech (noun, verb, adjective, etc.) for each word in a sentence.
Named Entity Recognition (NER): Identifying and categorizing entities in text, such as names, organizations, and locations.
Sentiment Analysis: Determining the sentiment (positive, negative, neutral) of a piece of text.
Text Classification: Categorizing text into predefined classes or categories.

Practical Examples

Sentiment Analysis

Sentiment analysis is a common application of NLP. It can be used to analyze customer feedback, social media posts, and more. Here's an example of a sentiment analysis model in action:

import nltk
from nltk.sentiment import SentimentIntensityAnalyzer

nltk.download('vader_lexicon')
sia = SentimentIntensityAnalyzer()

text = "I love this product! It's amazing."
sentiment_score = sia.polarity_scores(text)

print(sentiment_score)

Text Classification

Text classification is another important application of NLP. It can be used to categorize text into predefined classes, such as spam or not spam. Here's an example of a text classification model using scikit-learn:

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

# Sample data
data = [
    "This is a spam message",
    "I love this product",
    "Buy this now",
    "This is not spam",
    "I hate this product"
]

labels = [1, 0, 1, 0, 1]

# Preprocess data
vectorizer = CountVectorizer()
X = vectorizer.fit_transform(data)

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, labels, test_size=0.2)

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

# Evaluate model
accuracy = model.score(X_test, y_test)
print(f"Model accuracy: {accuracy}")