// Code Glimpse

Analyzing Shakespeare'S Text Using Natural Language Processing

Introduction

Natural Language Processing (NLP) is an emerging field that deals with the interaction between humans and computers using natural language. It has become an essential part of many communication and media analysis applications. In this blog post, we'll delve into a very random yet interesting topic: Analyzing the text of Shakespeare's works using NLP to find out the most common words used and their relationship with one another. We'll be using Python as our programming language with the libraries NLTK, spaCy, and the BeautifulSoup library for web scraping.

Getting Started: Downloading Shakespeare's Text

For our project, we'll need the complete text of Shakespeare's works. Our first step is to download the text of his work. Due to copyright issues, the works of Shakespeare are in the public domain, making them easily accessible for our project. We can obtain the text from the following site: https://www.gutenberg.org/cache/epub/100/pg100.xml

To download the text, let's use the requests library and extract the text using the BeautifulSoup library.

import requests
from bs4 import BeautifulSoup

url = "https://www.gutenberg.org/cache/epub/100/pg100.xml"
response = requests.get(url)
soup = BeautifulSoup(response.content, 'xml')
shakespeare_text = soup.get_text()

Pre-processing the Text

Now that we have our text, let's start pre-processing it. Pre-processing is an important step in any NLP project, as it helps to clean, normalize, and tokenize the text efficiently. For this project, we'll use the following methods of pre-processing:

  • Remove special characters and lowercase the text
  • Tokenize the text (convert sentences to a list of words)
  • Remove stop words
  • Perform stemming and lemmatization

Let's import the necessary libraries:

import string
import nltk
from nltk.corpus import stopwords
from nltk.stem import PorterStemmer
from nltk.tokenize import sent_tokenize, word_tokenize
import spacy

nltk.download('punkt')
nltk.download('stopwords')

Now let's define our pre-processing function:

def preprocess(text):
    # Remove special characters and convert to lowercase
    text = text.translate(str.maketrans('', '', string.punctuation)).lower()
    tokens = word_tokenize(text)
    
    # Remove stopwords
    stop_words = set(stopwords.words('english'))
    tokens = [word for word in tokens if word not in stop_words]
    
    # Perform stemming
    stemmer = PorterStemmer()
    tokens = [stemmer.stem(token) for token in tokens]
    
    # Perform lemmatization
    nlp = spacy.load('en_core_web_sm')
    tokens = ' '.join(tokens)
    doc = nlp(tokens)
    tokens = [token.lemma_ for token in doc]
    
    return tokens

Let's preprocess our Shakespeare text:

processed_text = preprocess(shakespeare_text)

Finding the Most Common Words

To find the most common words in the processed text, we can use the Counter class from the collections library.

from collections import Counter

word_count = Counter(processed_text)
most_common_words = word_count.most_common(10)
print(most_common_words)

This will output the top 10 most common words used in Shakespeare's works.

Visualizing the Results

To visualize the results, we can use a popular data visualization library called matplotlib.

import matplotlib.pyplot as plt

words, counts = zip(*most_common_words)
plt.bar(words, counts)
plt.xlabel('Words')
plt.ylabel('Frequency')
plt.title('Top 10 Most Common Words in Shakespeare Texts')
plt.show()

This will display a bar chart depicting the frequency of the most common words in Shakespeare's works.

In this blog post, we've successfully analyzed Shakespeare's text using NLP techniques. The pre-processing steps taken, such as removing special characters, lowercasing, tokenization, stopword removal, and stemming/lemmatization, all contribute to the efficiency of NLP workflows. We've also visualized the most common words used in Shakespeare's works through a simple bar chart using Python's matplotlib library.