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p25.bigramcloud.py
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p25.bigramcloud.py
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import PyPDF2
from wordcloud import WordCloud
import matplotlib.pyplot as plt
from collections import Counter
import nltk
from nltk.corpus import stopwords
from nltk.util import ngrams
import string
# Ensure you have the required NLTK data
nltk.download('punkt')
nltk.download('stopwords')
def read_pdf(file_path):
pdf_reader = PyPDF2.PdfReader(file_path)
text = ""
for page_num in range(len(pdf_reader.pages)):
page = pdf_reader.pages[page_num]
text += page.extract_text()
return text
def clean_text(text):
# Tokenize text
words = nltk.word_tokenize(text.lower())
# Remove punctuation and stopwords
stop_words = set(stopwords.words('english'))
words = [word for word in words if word.isalnum() and word not in stop_words]
return words
def get_bigrams(words):
bigrams = list(ngrams(words, 2))
bigram_freq = Counter(bigrams)
return bigram_freq
def create_word_cloud_from_bigrams(bigram_freq, output_image_path):
# Convert bigram tuples to strings
bigram_dict = {' '.join(bigram): freq for bigram, freq in bigram_freq.items()}
wordcloud = WordCloud(width=800, height=400, background_color='white').generate_from_frequencies(bigram_dict)
plt.figure(figsize=(10, 5))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.savefig(output_image_path)
plt.show()
if __name__ == "__main__":
pdf_path = './2025_MandateForLeadership_FULL.pdf'
output_image_path = 'bigram_word_cloud.png'
text = read_pdf(pdf_path)
words = clean_text(text)
bigram_freq = get_bigrams(words)
create_word_cloud_from_bigrams(bigram_freq, output_image_path)