|
from bs4 import BeautifulSoup |
|
import datetime |
|
import requests |
|
import nltk |
|
import json |
|
|
|
class WebScraper: |
|
def __init__(self, url): |
|
self.URL = url |
|
try: |
|
self.webpage = requests.get(self.URL) |
|
except requests.exceptions.RequestException as e: |
|
print(f"Error: {e}") |
|
if self.webpage: |
|
try: |
|
self.soup = BeautifulSoup(self.webpage.text, "html.parser") |
|
except: |
|
print("Error: Failed to create BeautifulSoup object.") |
|
|
|
def remove_unicode(self, string): |
|
return string.encode('ascii', 'ignore').decode('utf-8') |
|
|
|
def get_page_title(self): |
|
try: |
|
div_element = self.soup.find('div', class_='m-statement__quote') |
|
title = div_element.get_text(strip=True) |
|
cleaned_title = self.remove_unicode(title) |
|
cleaned_title = cleaned_title.replace('\"', '') |
|
except AttributeError: |
|
return None |
|
return cleaned_title |
|
|
|
def get_page_author(self): |
|
try: |
|
author_element = self.soup.find('div', class_='m-author__content').find('a') |
|
author = author_element.get_text(strip=True) |
|
except AttributeError: |
|
return None |
|
return author |
|
|
|
def get_page_posted_date(self): |
|
date_element = None |
|
try: |
|
date_element = self.soup.find('span', class_='m-author__date') |
|
date = date_element.get_text(strip=True) |
|
date_obj = datetime.datetime.strptime(date, "%B %d, %Y") |
|
formatted_date = date_obj.strftime("%m/%d/%Y") |
|
except (AttributeError, ValueError): |
|
return None |
|
return formatted_date |
|
|
|
def get_sci_check_digest(self): |
|
try: |
|
div_element = self.soup.find('div', class_='short-on-time') |
|
li_tags = div_element.find_all('li') if div_element else [] |
|
sci_digest_list = [li.get_text(strip=True) for li in li_tags] |
|
final_sci_digest = ", ".join(sci_digest_list) |
|
cleaned_sci_digest = self.remove_unicode(final_sci_digest) |
|
cleaned_sci_digest = cleaned_sci_digest.replace('\"', '') |
|
tokenised_sci_digest = nltk.sent_tokenize(cleaned_sci_digest) |
|
except AttributeError: |
|
return None |
|
return tokenised_sci_digest |
|
|
|
def get_paragraph_list(self): |
|
try: |
|
paragraph_list = [] |
|
article_element = self.soup.find('article', class_='m-textblock') |
|
p_elements = article_element.find_all('p') |
|
text_list = [p.get_text(strip=True) for p in p_elements] |
|
for text in text_list: |
|
paragraph_list.append(text) |
|
final_paragraphs = " ".join(paragraph_list) |
|
cleaned_paragraphs = final_paragraphs.replace('\u00a0', ' ') |
|
cleaned_paragraphs = self.remove_unicode(cleaned_paragraphs) |
|
cleaned_paragraphs = cleaned_paragraphs.replace('\"', '') |
|
tokenized_paragraphs = nltk.sent_tokenize(cleaned_paragraphs) |
|
except AttributeError: |
|
return None, None |
|
return paragraph_list, tokenized_paragraphs |
|
|
|
def get_sentences_citations(self): |
|
try: |
|
p_elements = self.soup.select('article.m-textblock p') |
|
citation_list = [] |
|
for p in p_elements: |
|
href = p.find('a') |
|
if href and 'href' in href.attrs: |
|
href_text = href['href'] |
|
sentence = p.get_text(strip=True) |
|
cleaned_sentence = sentence.replace('\u00a0', ' ') |
|
cleaned_sentence = self.remove_unicode(cleaned_sentence) |
|
cleaned_sentence = cleaned_sentence.replace('\"', '') |
|
citation_list.append({"sentence": cleaned_sentence, "hrefs": href_text}) |
|
except AttributeError: |
|
return None |
|
return citation_list |
|
|
|
def get_issue_list(self): |
|
issue_list = [] |
|
try: |
|
ul_element = self.soup.find('ul', class_='m-list--horizontal') |
|
li_elements = ul_element.find_all('li', class_='m-list__item') |
|
for li in li_elements[:-1]: |
|
category = li.a['title'] |
|
issue_list.append(category) |
|
except AttributeError: |
|
return None |
|
return issue_list |
|
|
|
def get_image_info(self): |
|
try: |
|
article_element = self.soup.find('article', class_='m-textblock') |
|
p_elements = article_element.find_all('p') |
|
em_elements = article_element.find_all('em') |
|
img_count = 0 |
|
image_captions = [] |
|
for p in p_elements: |
|
img_tag = p.find('img') |
|
if img_tag: |
|
img_src = img_tag['src'] |
|
if img_src: |
|
img_count += 1 |
|
if img_count <= len(em_elements): |
|
image_caption = em_elements[img_count - 1].get_text(strip=True) |
|
cleaned_captions = image_caption.replace('\u00a0', ' ') |
|
cleaned_captions = self.remove_unicode(cleaned_captions) |
|
cleaned_captions = cleaned_captions.replace('\"', '') |
|
image_captions.append({"image_src": img_src, "image_caption": cleaned_captions}) |
|
except: |
|
return None |
|
return image_captions |
|
|
|
def get_label(self): |
|
try: |
|
target = self.soup.find_all('div', attrs={'class':'m-statement__meter'}) |
|
for i in target: |
|
label = i.find('div', attrs={'class':'c-image'}).find('img').get('alt') |
|
|
|
|
|
|
|
|
|
except: |
|
return None |
|
return label |
|
|
|
with open("./income.json", "r") as infile: |
|
data = json.load(infile) |
|
urls = data["url"] |
|
labels = data["label"] |
|
|
|
scraped_data = [] |
|
for url, label in zip(urls,labels): |
|
print(url) |
|
scraper = WebScraper(url) |
|
data = { |
|
"url": url, |
|
"title": scraper.get_page_title(), |
|
"author": scraper.get_page_author(), |
|
"posted": scraper.get_page_posted_date(), |
|
"sci_digest": scraper.get_sci_check_digest(), |
|
"paragraphs": scraper.get_paragraph_list()[1], |
|
"issues": scraper.get_issue_list(), |
|
"image_data": scraper.get_image_info(), |
|
"data": scraper.get_sentences_citations(), |
|
"label": label |
|
} |
|
scraped_data.append(data) |
|
|
|
with open("./json_new/income.json", "w") as outfile: |
|
json.dump(scraped_data, outfile) |