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Generating Word Cloud in Python

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Word Cloud is a data visualization technique used for representing text data in which the size of each word indicates its frequency or importance. Significant textual data points can be highlighted using a word cloud. Word clouds are widely used for analyzing data from social network websites.

For generating word cloud in Python, modules needed are – matplotlib, pandas and wordcloud. To install these packages, run the following commands :

pip install matplotlib
pip install pandas
pip install wordcloud

The dataset used for generating word cloud is collected from UCI Machine Learning Repository. It consists of YouTube comments on videos of popular artists. 
Dataset Link :

Below is the implementation : 


# Python program to generate WordCloud
# importing all necessary modules
from wordcloud import WordCloud, STOPWORDS
import matplotlib.pyplot as plt
import pandas as pd
# Reads 'Youtube04-Eminem.csv' file
df = pd.read_csv(r"Youtube04-Eminem.csv", encoding ="latin-1")
comment_words = ''
stopwords = set(STOPWORDS)
# iterate through the csv file
for val in df.CONTENT:
    # typecaste each val to string
    val = str(val)
    # split the value
    tokens = val.split()
    # Converts each token into lowercase
    for i in range(len(tokens)):
        tokens[i] = tokens[i].lower()
    comment_words += " ".join(tokens)+" "
wordcloud = WordCloud(width = 800, height = 800,
                background_color ='white',
                stopwords = stopwords,
                min_font_size = 10).generate(comment_words)
# plot the WordCloud image                      
plt.figure(figsize = (8, 8), facecolor = None)
plt.tight_layout(pad = 0)

Output : 

The above word cloud has been generated using Youtube04-Eminem.csv file in the dataset. One interesting task might be generating word clouds using other csv files available in the dataset.

Advantages of Word Clouds : 

  1. Analyzing customer and employee feedback.
  2. Identifying new SEO keywords to target.

Drawbacks of Word Clouds : 

  1. Word Clouds are not perfect for every situation.
  2. Data should be optimized for context.

Reference :

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Last Updated : 05 Jul, 2021
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