title: Sentiment Analysis using NLTK and beautifulsoup
body: <p>I'm working on a personal project where I'm thinking of doing sentiment analysis using NLTK and Vader to compare presidential speeches.</p>
  <p>I was able to use beautiful soup to find one of George Washington's speeches and I managed to put the speech in a list. But after that, I'm not really sure the best way to go further. It seems that it's typical for the file to be read from a text file but I have the brackets that have the list which make it difficult. I'm not sure if I should store the web scraped speech in a file or just work at from the list. Or maybe I should put the speech into a dataframe already? I'm not too sure.</p>
  <pre><code>from bs4 import BeautifulSoup
  import requests
  import spacy
  import pandas as pd
  
  page_link = 'https://www.ourdocuments.gov/doc.php?flash=false&amp;doc=11&amp;page=transcript'
  page_response = requests.get(page_link, timeout=5)
  page_content = BeautifulSoup(page_response.content, &quot;html.parser&quot;)
  
  textContent = []
  for i in range(0, 7):
      paragraphs = page_content.find_all(&quot;p&quot;)[i].text
      textContent.append(paragraphs)
  
  toWrite = open('washington.txt', 'w')
  line = textContent
  toWrite.write(str(line))
  toWrite.close()
  </code></pre>
  <p>Any help or pointers would be greatly appreciated.</p>