In this section we will look at how to create a word cloud using Python. Yes there are lots of examples out there that show this, but none of them worked for me. This could be due to those examples using the older version of Python, libraries/packages no long exist, etc. There are lots of possible reasons. So I have to piece it together and the code given below is what I ended up with. Some steps could be skipped but this is what I ended up with.
Step 1 – Read in the data
In my example I wanted to create a word cloud for a website, so I picked my own blog for this exercise/example. The following code is used to read the website (a list of all packages used is given at the end).
import nltk from urllib.request import urlopen from bs4 import BeautifulSoup url = "http://www.oralytics.com/" html = urlopen(url).read() print(html)
The last line above, print(html), isn’t needed, but I used to to inspect what html was read from the webpage.
Step 2 – Extract just the Text from the webpage
The Beautiful soup library has some useful functions for processing html. There are many alternative ways of doing this processing but this is the approached that I liked.
The first step is to convert the downloaded html into BeautifulSoup format. When you view this converted data you will notices how everything is nicely laid out.
The second step is to remove some of the scripts from the code.
soup = BeautifulSoup(html) print(soup) # kill all script and style elements for script in soup(["script", "style"]): script.extract() # rip it out print(soup)
Step 3 – Extract plain text and remove whitespacing
The first line in the following extracts just the plain text and the remaining lines removes leading and trailing spaces, compacts multi-headlines and drops blank lines.
text = soup.get_text() print(text) # break into lines and remove leading and trailing space on each lines = (line.strip() for line in text.splitlines()) # break multi-headlines into a line each chunks = (phrase.strip() for line in lines for phrase in line.split(" ")) # drop blank lines text = '\n'.join(chunk for chunk in chunks if chunk) print(text)
Step 4 – Remove stop words, tokenise and convert to lower case
As the heading says this code removes standard stop words for the English language, removes numbers and punctuation, tokenises the text into individual words, and then converts all words to lower case.
#download and print the stop words for the English language from nltk.corpus import stopwords #nltk.download('stopwords') stop_words = set(stopwords.words('english')) print(stop_words) #tokenise the data set from nltk.tokenize import sent_tokenize, word_tokenize words = word_tokenize(text) print(words) # removes punctuation and numbers wordsFiltered = [word.lower() for word in words if word.isalpha()] print(wordsFiltered) # remove stop words from tokenised data set filtered_words = [word for word in wordsFiltered if word not in stopwords.words('english')] print(filtered_words)
Step 5 – Create the Word Cloud
Finally we can create a word cloud backed on the finalised data set of tokenised words. Here we use the WordCloud library to create the word cloud and then the matplotlib library to display the image.
from wordcloud import WordCloud import matplotlib.pyplot as plt wc = WordCloud(max_words=1000, margin=10, background_color='white', scale=3, relative_scaling = 0.5, width=500, height=400, random_state=1).generate(' '.join(filtered_words)) plt.figure(figsize=(20,10)) plt.imshow(wc) plt.axis("off") plt.show() #wc.to_file("/wordcloud.png")
We get the following word cloud.
Step 6 – Word Cloud based on frequency counts
Another alternative when using the WordCloud library is to generate a WordCloud based on the frequency counts. For this you need to build up a table containing two items. The first item is the distinct token and the second column contains the number of times that word/token appears in the text. The following code shows this code and the code to generate the word cloud based on this frequency count.
from collections import Counter # count frequencies cnt = Counter() for word in filtered_words: cnt[word] += 1 print(cnt) from wordcloud import WordCloud import matplotlib.pyplot as plt wc = WordCloud(max_words=1000, margin=10, background_color='white', scale=3, relative_scaling = 0.5, width=500, height=400, random_state=1).generate_from_frequencies(cnt) plt.figure(figsize=(20,10)) plt.imshow(wc) #plt.axis("off") plt.show()
Now we get the following word cloud.
When you examine these word cloud to can easily guess what the main contents of my blog is about. Machine Learning, Oracle SQL and coding.
What Python Packages did I use?
Here are the list of Python libraries that I used in the above code. You can use PIP3 to install these into your environment.
nltk url open BeautifulSoup wordcloud Counter