Try to use select() instead. CSS selectors are more flexible. CSS selectors reference.
Have a look at SelectorGadget Chrome extension to grab CSS selectors by clicking on the desired element in your browser.
If you want to get all titles and so on, then you are looking for this container:
soup.select('.dbsr')
Make sure to pass user-agent, because Google might block your requests eventually and you'll receive a different HTML thus empty output. Check what is your user-agent
Pass user-agent:
headers = {
"User-agent":
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"
}
requests.get("YOUR_URL", headers=headers)
I'm not sure what exactly are you trying to do but a solution from Guven Degirmenci is a bit overkill as he mentioned, with slicing, regex, doing something in div#main. It's much simpler.
Code and example in the online IDE:
from textblob import TextBlob
import requests
from bs4 import BeautifulSoup
headers = {
"User-agent":
"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582"
}
class Analysis:
def __init__(self, term):
self.term = term
self.subjectivity = 0
self.sentiment = 0
self.url = f"https://www.google.com/search?q={self.term}&tbm=nws"
def run (self):
response = requests.get(self.url, headers=headers)
soup = BeautifulSoup(response.text, "html.parser")
news_data = []
for result in soup.select('.dbsr'):
title = result.select_one('.nDgy9d').text
link = result.a['href']
source = result.select_one('.WF4CUc').text
snippet = result.select_one('.Y3v8qd').text
date_published = result.select_one('.WG9SHc span').text
news_data.append({
"title": title,
"link": link,
"source": source,
"snippet": snippet,
"date_published": date_published
})
for h in news_data:
blob = TextBlob(f"{h['title']} {h['snippet']}")
self.sentiment += blob.sentiment.polarity / len(news_data)
self.subjectivity += blob.sentiment.subjectivity / len(news_data)
a = Analysis("Lasagna")
a.run()
print(a.term, "Subjectivity: ", a.subjectivity, "Sentiment: " , a.sentiment)
# Vaccine Subjectivity: 0.3255952380952381 Sentiment: 0.05113636363636363
# Lasagna Subjectivity: 0.36556818181818185 Sentiment: 0.25386093073593075
Alternatively, you can achieve the same thing by using Google News Results API from SerpApi. It's a paid API with a free plan.
The difference in your case is that you don't have to maintain the parser, figure out how to parse certain elements or figuring out why something isn't working as it should, and understand how to bypass blocks from Google. All that needs to be done is to iterate over structured JSON and get what you want fast.
Code integrated with your example:
from textblob import TextBlob
import os
from serpapi import GoogleSearch
class Analysis:
def __init__(self, term):
self.term = term
self.subjectivity = 0
self.sentiment = 0
self.url = f"https://www.google.com/search"
def run (self):
params = {
"engine": "google",
"tbm": "nws",
"q": self.url,
"api_key": os.getenv("API_KEY"),
}
search = GoogleSearch(params)
results = search.get_dict()
news_data = []
for result in results['news_results']:
title = result['title']
link = result['link']
snippet = result['snippet']
source = result['source']
date_published = result['date']
news_data.append({
"title": title,
"link": link,
"source": source,
"snippet": snippet,
"date_published": date_published
})
for h in news_data:
blob = TextBlob(f"{h['title']} {h['snippet']}")
self.sentiment += blob.sentiment.polarity / len(news_data)
self.subjectivity += blob.sentiment.subjectivity / len(news_data)
a = Analysis("Vaccine")
a.run()
print(a.term, "Subjectivity: ", a.subjectivity, "Sentiment: " , a.sentiment)
# Vaccine Subjectivity: 0.30957251082251086 Sentiment: 0.06277056277056277
# Lasagna Subjectivity: 0.30957251082251086 Sentiment: 0.06277056277056277
P.S - I wrote a bit more detailed blog post about how to scrape Google News.
Disclaimer, I work for SerpApi.
