How to create a wordcloud and what is it helpful for Explain with an example?

This recipe helps you create a wordcloud and what is it helpful for Explain with an example

Recipe Objective

How to create a wordcloud and what is it helpful for?

Wordcloud is nothing but a data visualization technique mainly used for text representation it is also called a tag cloud. In this, the size of each word indicates its frequency or importance of that word. It displays a list of words, the importance of each is shown by font color or size.

What is it useful for: Analyzing text data from social media websites. Significant textual points can be highlighted using a word cloud. In a Customer service process useful to analyze customer feedback. Identifying new SEO(Search engine optimization) Keyword to target. And Many More...

Hands-On Guide to the Art of Tuning Locality Sensitive Hashing in Python

Step 1 - Install Wordcloud

!pip install wordcloud

Step 2 - Import the necessary libraries

from wordcloud import WordCloud, STOPWORDS import matplotlib.pyplot as plt import pandas as pd

Step 3 - Take a sample data set

df_sample = pd.read_csv('/content/Youtube_Comments_data.csv', encoding ="latin-1") df_sample.head()

For sample data we are using youtube comments data on videos of famous artist.

Step 4 - Store comments in a simple string and stopwords in a variable

words_comments = '' My_stopwords = set(STOPWORDS)

Step 5 - Iterate through the Sample data.

for elements in df_sample.CONTENT: elements = str(elements) tokenization = elements.split() for i in range(len(tokenization)): tokenization[i] = tokenization[i].lower() words_comments = words_comments + " ".join(tokenization)+" "

Here in the above in first for loop we are firstly typecasting the each element into string then splitting the values. After that in the second for loop we are converting each value into lower case.

Step 5 - Create wordcloud for visualization

My_wordcloud = WordCloud(width = 800, height = 800, background_color ='white', stopwords = My_stopwords, min_font_size = 10).generate(words_comments)

Step 6 - Plot the cloud Image

plt.figure(figsize = (8, 8), facecolor = None) plt.imshow(My_wordcloud) plt.axis("off") plt.tight_layout(pad = 0) plt.show()

What Users are saying..

profile image

Abhinav Agarwal

Graduate Student at Northwestern University
linkedin profile url

I come from Northwestern University, which is ranked 9th in the US. Although the high-quality academics at school taught me all the basics I needed, obtaining practical experience was a challenge.... Read More

Relevant Projects

Deploy Transformer-BART Model on Paperspace Cloud
In this MLOps Project you will learn how to deploy a Tranaformer BART Model for Abstractive Text Summarization on Paperspace Private Cloud

Build a Multi-Class Classification Model in Python on Saturn Cloud
In this machine learning classification project, you will build a multi-class classification model in Python on Saturn Cloud to predict the license status of a business.

AWS MLOps Project for Gaussian Process Time Series Modeling
MLOps Project to Build and Deploy a Gaussian Process Time Series Model in Python on AWS

Classification Projects on Machine Learning for Beginners - 2
Learn to implement various ensemble techniques to predict license status for a given business.

End-to-End Snowflake Healthcare Analytics Project on AWS-2
In this AWS Snowflake project, you will build an end to end retraining pipeline by checking Data and Model Drift and learn how to redeploy the model if needed

CycleGAN Implementation for Image-To-Image Translation
In this GAN Deep Learning Project, you will learn how to build an image to image translation model in PyTorch with Cycle GAN.

Classification Projects on Machine Learning for Beginners - 1
Classification ML Project for Beginners - A Hands-On Approach to Implementing Different Types of Classification Algorithms in Machine Learning for Predictive Modelling

Build a Music Recommendation Algorithm using KKBox's Dataset
Music Recommendation Project using Machine Learning - Use the KKBox dataset to predict the chances of a user listening to a song again after their very first noticeable listening event.

Avocado Machine Learning Project Python for Price Prediction
In this ML Project, you will use the Avocado dataset to build a machine learning model to predict the average price of avocado which is continuous in nature based on region and varieties of avocado.

Build a Credit Default Risk Prediction Model with LightGBM
In this Machine Learning Project, you will build a classification model for default prediction with LightGBM.