Vec = CountVectorizer(stop_words = ‘english’).fit(corpus) # Visualizing unigramsįrom sklearn.feature_extraction.text import CountVectorizer This helps us to understand which words most occur together and make our text cleaner and understanding the text distribution. N-Gram is used to describe the number of words used as observation points, e.g., unigram means singly-worded, bigram means the 2-worded phrase, and trigram means 3-worded phrase. We will start extracting N-Gram features and see their distribution. Visualizing the unigram, bigram, and trigram on the text data This is a word cloud generated from the AI articles collected and after custom filtering words – Source: Omdena When we print the variable text it’s like: # Join all text into one to start cleaning them together for making word cloud Results = soup.find_all(“div”, attrs = ).get_text() Let’s start cleaning our downloaded data! # we are finding all data under division tags with class ZINbbc stopword_list = stopwords.words(‘english’) So these words (tokenizing the text) must be removed which helps to reduce the features from our data. Stopwords include: I, he, she, and, but, was were, being, have, etc, which do not add meaning to the data. Remove special characters using regex pattern = r’’ if not remove_digits else r’’ re.sub(pattern, ‘’, text)Ħ. Convert data in lower or upper text.lower()ĥ. Remove punctuations re.sub(“”, “” , text)Ĥ. Remove extra spaces re.sub(“s+”,” “, doc)ģ. Remove the brackets then use regex re.sub(‘.*?]’, ‘ ‘, text)Ģ.
We know that regular expressions are useful to replace or remove characters.ġ. Now we have all the articles but they are in HTML format, which we have to clean and transform in a format that we can analyze and visualize through a word cloud.īefore we will revise a few of the regex concepts. Cleaning the data using regex & generating a word cloud Web scraping resources – Source: octaparse 2. Soup = BeautifulSoup(ntent, ‘html.parser’) Start scraping the articles using the URL we built above. To scrape 1000 articles, clean and visualize them using a word cloud. If you only want 10 results shown, num=10 Num - controls the number of results shown.
There’s a whole lot of other things one can match.įor instance, app for applications, blg for blogs, bks for books, isch for images, plcs for places, vid for videos, shop for shopping, and rcp for recipes. Tbm - to be matched, here we need tbm=nws to search for news items. Hl - the interface language, i.e., hl=en for English Q - this is the query topic, i.e., q=AI if you’re searching for Artificial Intelligence news Url =""+topic+"&tbm=nws&hl=en&num="+str(numResults) If we want to scrape articles from Google news, there are a few parameters that we can use to build a search query.Īll Google search URLs start with # Build a query Scraping news articles on “AI” from Google news
Visualization of the unigram, bigram, and trigram on the text dataġ.Cleaning the data using regex and generate word cloud on clean text.Scraping news articles on “AI” from Google news.Let´s start with the concepts and build the final project in the six steps listed below: Here is a list of the topics covered and an interesting project, which we will build step by step. Visualization is also one of the very important steps of all Machine Learning projects and we will cover this and some analyses in the tutorial. Regular expressions are extremely useful in extracting information from any text by searching for one or more matches of a specific pattern.įields of applications range from validation to parsing/replacing strings, to translating data to other formats, NLP, and web scraping.
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