Creating a Word Cloud using Python

    May 21, 2018 8:16:00 AM by Brendan Tierney

    Over the past few days I've been doing a bit more playing around with Python, and create a word cloud. 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 = ""
    html = urlopen(url).read()

    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)
    # kill all script and style elements
    for script in soup(["script", "style"]):
    script.extract() # rip it out



    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()
    # 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)



    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'stopwords')
    stop_words = set(stopwords.words('english'))

    #tokenise the data set
    from nltk.tokenize import sent_tokenize, word_tokenize
    words = word_tokenize(text)

    # removes punctuation and numbers
    wordsFiltered = [word.lower() for word in words if word.isalpha()]

    # remove stop words from tokenised data set
    filtered_words = [word for word in wordsFiltered if word not in stopwords.words('english')]


    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))

    We get the following word cloud.

    Tags: Oracle Analysis

    Brendan Tierney

    Written by Brendan Tierney

    Brendan Tierney, Oracle ACE Director, is an independent consultant and lectures on Data Mining and Advanced Databases in the Dublin Institute of Technology in Ireland. He has 22+ years of extensive experience working in the areas of Data Mining, Data Warehousing, Data Architecture and Database Design. Brendan has worked on projects in Ireland, UK, Belgium and USA. Brendan is the editor of the UKOUG Oracle Scene magazine and deputy chair of the OUG Ireland BI SIG. Brendan is a regular speaker at conferences across Europe and the USA and has written technical articles for OTN, Oracle Scene, IOUG SELECT Journal and ODTUG Technical Journal. Brendan has published the following books with Oracle Press book Predictive Analytics using Oracle Data Miner Oracle R Enterprise SQL & PL/SQL from the Experts These books are available on Amazon.