Natural Language Processing (NLP)

 Natural Language Processing (NLP)

NLP involves using machine learning and other techniques to analyze and understand human language. It typically involves using NLP libraries such as NLTK, spaCy, or Gensim to process and analyze text data.

Example of sentiment analysis using NLTK in Python:

Install the NLTK library and download the necessary data.

Load and preprocess the text data using NLTK.

Use NLTK's sentiment analysis tools to classify the sentiment of the text.

Visualize the results using matplotlib or other visualization libraries.

import nltk

from nltk.sentiment import SentimentIntensityAnalyzer

import matplotlib.pyplot as plt

# Download the necessary data

nltk.download('vader_lexicon')

# Load and preprocess the text data

text = "I really enjoyed this movie, it was great!"

tokens = nltk.word_tokenize(text)

# Use NLTK's sentiment analysis tools

sia = SentimentIntensityAnalyzer()

scores = sia.polarity_scores(text)

# Visualize the results

labels = ['Negative', 'Neutral', 'Positive']

sizes = [scores['neg'], scores['neu'], scores['pos']]

colors = ['red', 'gray', 'green']

plt.pie(sizes, labels=labels, colors=colors, autopct='%1.1f%%')

plt.axis('equal')

plt.show()