The task is to count the most frequent words, which extracts data from dynamic sources.
First, create a web crawler or scraper with the help of the requests module and a beautiful soup module, which will extract data from the web pages and store them in a list. There might be some undesired words or symbols (like special symbols, blank spaces), which can be filtered in order to ease the counts and get the desired results.
After counting each word, we also can have the count of most (say 10 or 20) frequent words.
Modules and Library functions used :
requests : Will allow you to send HTTP/1.1 requests and many more.
beautifulsoup4 : Used for parsing HTML/XML to extract data out of HTML and XML files.
operator : Exports a set of efficient functions corresponding to the intrinsic operators.
collections : Implements high-performance container datatypes.
Below is an implementation of the idea discussed above :
Python3
# Python3 program for a word frequency# counter after crawling/scraping a web-pageimport requestsfrom bs4 import BeautifulSoupimport operatorfrom collections import Counter'''Function defining the web-crawler/corespider, which will fetch information froma given website, and push the contents tothe second function clean_wordlist()'''def start(url): # empty list to store the contents of # the website fetched from our web-crawler wordlist = [] source_code = requests.get(url).text # BeautifulSoup object which will # ping the requested url for data soup = BeautifulSoup(source_code, 'html.parser') # Text in given web-page is stored under # the <div> tags with class <entry-content> for each_text in soup.findAll('div', {'class': 'entry-content'}): content = each_text.text # use split() to break the sentence into # words and convert them into lowercase words = content.lower().split() for each_word in words: wordlist.append(each_word) clean_wordlist(wordlist)# Function removes any unwanted symbolsdef clean_wordlist(wordlist): clean_list = [] for word in wordlist: symbols = "!@#$%^&*()_-+={[}]|\;:\"<>?/., " for i in range(len(symbols)): word = word.replace(symbols[i], '') if len(word) > 0: clean_list.append(word) create_dictionary(clean_list)# Creates a dictionary containing each word's# count and top_20 occurring wordsdef create_dictionary(clean_list): word_count = {} for word in clean_list: if word in word_count: word_count[word] += 1 else: word_count[word] = 1 ''' To get the count of each word in the crawled page --> # operator.itemgetter() takes one # parameter either 1(denotes keys) # or 0 (denotes corresponding values) for key, value in sorted(word_count.items(), key = operator.itemgetter(1)): print ("% s : % s " % (key, value)) <-- ''' c = Counter(word_count) # returns the most occurring elements top = c.most_common(10) print(top)# Driver codeif __name__ == '__main__': # starts crawling and prints output start(url) |
[('to', 10), ('in', 7), ('is', 6), ('language', 6), ('the', 5),
('programming', 5), ('a', 5), ('c', 5), ('you', 5), ('of', 4)]
