Creating word clouds of various shapes
Introduction
World clouds are the visual representation of text data of text data, typically used to depict keyword metadata on websites, or to visualize free form text. Google says a word cloud is “an image composed of words used in a particular text or subject, in which the size of each word indicates its frequency or importance.”
The more often a specific words appears in your text, the bigger and bolder it appears in your word cloud. This article will show how to create default world cloud and different shapes of world cloud to make it more interesting.
Install the dependencies
!pip install wordcloud --quiet
!pip install stop_words --quietimport pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import nltk
from nltk.corpus import stopwords
from nltk import sent_tokenize, word_tokenize
from wordcloud import WordCloud, STOPWORDS
from collections import Counter
from nltk.tokenize import RegexpTokenizer
from stop_words import get_stop_words
import re
from PIL import Imagenltk.download('stopwords')
nltk.download('punkt')[nltk_data] Downloading package stopwords to
[nltk_data] /Users/shridhar/nltk_data...
[nltk_data] Package stopwords is already up-to-date!
[nltk_data] Downloading package punkt to /Users/shridhar/nltk_data...
[nltk_data] Package punkt is already up-to-date!
True
Data
The data contains google news notifications extracted from dataset. The sentimental analysis has been performed on this dataset using [EmPushy docker preprocessing image](https://empushy.com/login.html), 1 being postive and -1 being negative sentiments. Let’s import the dataset and see few rows of data.
data = pd.read_csv('google_news_dataset_up.csv')data.head()
Preprocessing of text field
As we are crating word cloud, we nee to preprocess the text field by removing stopwords, numbers, duplicates and words with character length less than 2. To do so we make use of nltk library stopwords and punctuation.
data_positive = data[data['Setiment_overall']==1]
data_negative = data[data['Setiment_overall']==-1]def clean_data(data):
top_N = 100
#convert list of list into text
#a=''.join(str(r) for v in df_usa['title'] for r in v)
a = data['title'].str.lower().str.cat(sep=' ')
# removes punctuation,numbers and returns list of words
b = re.sub('[^A-Za-z]+', ' ', a)
#remove all the stopwords from the text
stop_words = list(get_stop_words('en'))
nltk_words = list(stopwords.words('english'))
stop_words.extend(nltk_words)
word_tokens = word_tokenize(b)
filtered_sentence = [w for w in word_tokens if not w in stop_words]
filtered_sentence = []
for w in word_tokens:
if w not in stop_words:
filtered_sentence.append(w)
# Remove characters which have length less than 2
without_single_chr = [word for word in filtered_sentence if len(word) > 2]
# Remove numbers
cleaned_data_title = [word for word in without_single_chr if not word.isnumeric()]
return cleaned_data_title
Top frequent words positive sentences
cleaned_data_title_pos = clean_data(data_positive)
# Calculate frequency distribution
word_dist = nltk.FreqDist(cleaned_data_title_pos)
rslt = pd.DataFrame(word_dist.most_common(top_N),
columns=['Word', 'Frequency'])
plt.figure(figsize=(10,10))
sns.set_style("whitegrid")
ax = sns.barplot(x="Word",y="Frequency", data=rslt.head(7))
Top frequent words negative sentences
cleaned_data_title_neg = clean_data(data_negative)
# Calculate frequency distribution
word_dist = nltk.FreqDist(cleaned_data_title_neg)
rslt = pd.DataFrame(word_dist.most_common(top_N),
columns=['Word', 'Frequency'])
plt.figure(figsize=(10,10))
sns.set_style("whitegrid")
ax = sns.barplot(x="Word",y="Frequency", data=rslt.head(7))
Wordcloud
Define a function to create a wordcloud using WordCloud library.
def wc(data,bgcolor,title, mask):
plt.figure(figsize = (50,50))
wc = WordCloud(width = 3000, height = 2000,background_color = bgcolor, max_words = 500, max_font_size = 100,
colormap='rainbow',stopwords=STOPWORDS,mask=mask)
wc.generate(' '.join(data))
plt.imshow(wc)
plt.axis('off')Word cloud without mask
wc(cleaned_data_title_pos,'black','Frequent Words',mask=None )
wc(cleaned_data_title_neg,'black','Frequent Words',mask=None )
Word cloud with mask
Masks are the shapes used to create a word cloud. Mask should be white background and can download from google. Here I have downloaded thumbs-up and thumbs-down mask to show positive and negative words used in the text.
img = Image.open('upvote_up.png')
img.resize((20, 20), Image.ANTIALIAS)
plt.imshow(img)<matplotlib.image.AxesImage at 0x7f9cf79aadf0>
img = Image.open('downvote_up.png')
img.resize((20, 20), Image.ANTIALIAS)
plt.imshow(img)<matplotlib.image.AxesImage at 0x7f9d00e335b0>
Now call the function wc created before and send mask as an argument as I have already created function to facilitate the mask.
Positive sentiment using thumbs up mask
mask_pos = np.array(Image.open('upvote_up.png'))wc(cleaned_data_title_pos,'black','Common Words', mask_pos)
Negative sentiment using thumbs down mask
mask_neg = np.array(Image.open('downvote_up.png'))wc(cleaned_data_title_neg,'black','Common Words', mask_neg)
Comment mask to show most words used in comments
mask_com = np.array(Image.open('comment.png'))wc(cleaned_data_title_pos,'black','Common Words', mask_com)
Star mask to show common words in the comments selected as Editor`s pick
mask_star = np.array(Image.open('star.png'))wc(cleaned_data_title_pos,'black','Common Words', mask_star)
Location mask to show most common locations of the commenters
In this dataset I do not have location, so Iam using few words only to create it.
mask_loc = np.array(Image.open('loc.png'))wc(cleaned_data_title_pos,'black','Common Words', mask_loc)
User mask to show Usernames with most comments
mask_user = np.array(Image.open('user.png'))wc(cleaned_data_title_pos,'black','Common Words', mask_user)
Function to save the image
def wc(data,bgcolor,title, mask):
plt.figure(figsize = (50,50))
wc = WordCloud(width = 3000, height = 2000,background_color = bgcolor, max_words = 500, max_font_size = 100,
colormap='rainbow',stopwords=STOPWORDS,mask=mask)
wc.generate(' '.join(data))
plt.imshow(wc)
plt.axis('off')
plt.savefig('data_ps_white.jpg')Conclusion
As demonstrated we can create word clouds of any shape using masks which are great and effective of visualisation.
