Lecture 79:- ML Sentiment Analysis - Preparing World Cloud

Creating a word cloud is a visually appealing way to visualize the most frequently occurring words in a text corpus. In the context of sentiment analysis, you can create separate word clouds for different sentiment categories (positive, negative, neutral) to understand which words are associated with each sentiment. Here's how you can prepare and create word clouds for sentiment analysis:

1. Text Preprocessing:

   • Before creating word clouds, preprocess the text data as mentioned earlier (tokenization, lowercase, punctuation removal, stop words removal, etc.).

2. Separate Sentiment Categories:

   • Divide your dataset into different sentiment categories (positive, negative, neutral).

3. Word Frequency Calculation:

   • Calculate the frequency of each word in each sentiment category. You can use libraries like collections.Counter to count word occurrences.

4. Word Cloud Generation:

   • Use a word cloud generation library like wordcloud to create word clouds based on word frequencies.

Here's an example code snippet to help you get started using Python and the wordcloud library:

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import pandas as pd import matplotlib.pyplot as plt from wordcloud import WordCloud from collections import Counter # Load your preprocessed dataset (divided by sentiment categories) positive_text = ... # List of preprocessed positive text samples negative_text = ... # List of preprocessed negative text samples neutral_text = ... # List of preprocessed neutral text samples # Function to generate and display a word cloud def generate_word_cloud(text, title): wordcloud = WordCloud(width=800, height=400, background_color='white').generate(' '.join(text)) plt.figure(figsize=(10, 5)) plt.imshow(wordcloud, interpolation='bilinear') plt.title(title) plt.axis('off') plt.show() # Generate word clouds for each sentiment category generate_word_cloud(positive_text, 'Positive Sentiment Word Cloud') generate_word_cloud(negative_text, 'Negative Sentiment Word Cloud') generate_word_cloud(neutral_text, 'Neutral Sentiment Word Cloud')

In this example, replace positive_text, negative_text, and neutral_text with your preprocessed text samples for each sentiment category.

This code generates separate word clouds for positive, negative, and neutral sentiments based on the word frequencies in your dataset. The size of each word in the word cloud is proportional to its frequency in the text corpus.

Analyzing these word clouds can help you understand which words are most strongly associated with each sentiment category. This can provide insights into the language patterns and common expressions used in different sentiment contexts.