Twitter Profile Descriptions Word Cloud from 2017 Dump using Python 3

Just a quick post to display a word cloud I created as a demo for my Adv Programming students using the Python WordCloud module. I was surprised to find that “Founder” was the top occurring word in the profile descriptions. What does this tell us?

I first had to write a PHP script to grab all the tweet objects from Twitter Search API that were captured by I then stored all the unique author profiles in a separate table from the tweets, which gave me approximately 3.4 million unique Twitter users who tweeted about science as captured by Of these 3.4 million, there were approximately 2.8 million users who had some characters in their Twitter profile description field.

To return results from the description found in my MySQL table of author profiles, I used the following query because I wanted to remove hard returns and tabs from the profile descriptions.

SELECT REPLACE(REPLACE(REPLACE(TRIM(`description`), '\r', ' '), '\n', ' '), '\t', ' ') 
FROM `profiles` 
WHERE TRIM(`description`)!='';

Next, we have the actual Python 3 code I used to create the WordCloud from the 2.8 million user descriptions. You’ll note I added a few extra terms to the STOPWORDS list because I ran this multiple times and found these terms that I wanted to remove from the final version.

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
Created on Sun Feb 10 16:19:56 2019
@author: tdbowman
import io
import csv
import numpy as np
from wordcloud import WordCloud, STOPWORDS
from os import path
from PIL import Image

# current directory
currdir = path.dirname(__file__)

# from
def create_wordcloud(text):

    # use cloud.png as mask for word cloud
    mask = np.array(, "cloud.png")))
    # create set of stopwords	
    stop_words = ["https", "co", "RT", "del", "http", 
                  "tweet", "tweets", "twitter", "en", "el", "us", "et",
                  "lo", "will", "ex", "de", "la", "rts"] + list(STOPWORDS)
    # create wordcloud object
    wc = WordCloud(background_color="white",
    # generate wordcloud
    # save wordcloud
    wc.to_file(path.join(currdir, "wc.png"))
if __name__ == "__main__":

    # Grab text from file and convert to list
    your_list = []
    with'all_descriptions.csv', 'r', encoding='utf-8') as f:
        reader = csv.reader(x.replace('\0', '') for x in f)
        your_list = ','.join([i[0] for i in reader])    
    # generate wordcloud

It could use some cleanup and the image could be higher resolution, but it’s a good example for the students how to utilize Python to create a word cloud.

Harvesting Images from Site for Study

I needed to write code that would allow me to harvest images from a site where the images were displayed 10 per page over n number of pages.  I wanted to set it up so that I could start it and let it run over time and harvest images.  This immediately meant I’d be working with PHP and jQuery using AJAX.  I’ve written another post titled Web Scraping Using PHP and jQuery about this type of AJAX script and I needed to use what I’d learned here to implement this new scraping engine.

The reason I’m writing this code is so that we can start a few studies on selfies.

I ended up using a bookmarklet, which is a JavaScript snippet that you can add to a browser as a bookmark. We had assistants visit the pages where the images were stored and click on the bookmark to harvest images and metadata associated with each image. While it is a bit cumbersome, it was the easiest and quickest way to start collecting the images for our project. I wrote the code such that the JavaScript would speak to the PHP code and the PHP code would handle all the heavy lifting (saving image and scraping page for metadata). The images and text files were automatically saved to a Dropbox folder using the Dropbox API.