Generating Word Cloud in Python

A Word Cloud is a picture made up of words where the size of each word shows how frequently it appears in the dataset i.e The bigger the word appears in the cloud the more often that word is used. They help us to identify the most common and important words in a text. In this article, we will understand about word cloud and how to generate it using Python.

For Example: If we analyze customer reviews of a movie like "good", "bad" or "average" might be bigger if they are mentioned many times. These are useful because they:

• Quickly show the most common words in text data
• Help to understand what people are talking about in large text files
• Make text data look visually appealing
• Allow easy identification of important words

Implementing Word Cloud in Python

We will be using IMDB dataset and this dataset contains 50,000 movie reviews in CSV format. We will focus on the review column which contains the text data of the movie reviews. You can download it from here. Below is the step by step implementation:

Step 1: Loading the Dataset

Let's load the dataset using pandas. 

import pandas as pd

df = pd.read_csv('/content/IMDB-Dataset.csv')

print(df.head())

Output:

Step 2: Understanding the Dataset

Before cleaning the text let's understand the dataset. The dataset contains two columns:

• review: Contains the movie review text
• sentiment: It shows whether the review is positive or negative

We are only interested in the review column. Let's check the column names and some sample text.

print(df.columns)

print(df['review'][0])

Output:

Index(['review', 'sentiment'], dtype='object') One of the ....your darker side.

The review column contains detailed text reviews of movies. Our goal is to extract the most frequent words from these reviews.

Step 3: Cleaning the Text Data

Before generating the word cloud, we need to clean the text data which involves:

1. Removing punctuation

2. Converting text to lowercase

3. Removing stopwords i.e common words like "the", "is", "and"

• re.sub(): This removes punctuation and numbers
• STOPWORDS: These are list of common stopwords

import re
from wordcloud import STOPWORDS

text = ' '.join(df['review'].astype(str).tolist())

text = re.sub(r'[^A-Za-z\s]', '', text)

text = text.lower()

stopwords = set(STOPWORDS)
text = ' '.join(word for word in text.split() if word not in stopwords)

Step 4: Generating the Word Cloud

Now our text is clean, let's generate the word cloud.

• WordCloud(): Generates the word cloud

from wordcloud import WordCloud
import matplotlib.pyplot as plt

wordcloud = WordCloud(width=800, height=400, background_color='white').generate(text)

plt.figure(figsize=(10, 5))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')  
plt.title("IMDB Movie Reviews Word Cloud")
plt.show()

Output:

Step 5: Customizing the Word Cloud

We can customize the word cloud with different options like:

1. Maximum number of words

2. Color scheme

3. Shape of the cloud

• max_words: Limits the number of words
• colormap: Changes the color of the word cloud

wordcloud = WordCloud(width=800, height=400, background_color='white', max_words=100, colormap='coolwarm').generate(text)

plt.figure(figsize=(10, 5))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis('off')
plt.title("Customized IMDB Movie Reviews Word Cloud")
plt.show()

Output:

Real life applications of Word Cloud

• Sentiment Analysis: Imagine we have hundreds of customer reviews. By creating two word clouds one for positive words like "great" and "friendly" and another for negative words like "late" and "broken" we can easily see what customers like or dislike.
• Social Media Analysis: Observing what's trending on social media by collecting hashtags and keywords, word clouds can visually highlight what's being talked about the most.
• Real-Time Data: In live customer chats or support systems it can instantly show common issues like "delivery delay" or "payment error" which helps teams to respond faster.

By combining word clouds with NLP techniques we can see patterns, understand customer needs and make smarter data-driven decisions.