Section 1: Collecting Twitter Data
This section covers how to use Tweepy to collect tweets based on specific criteria, such as keywords, hashtags, or usernames. It explains the different parameters and methods available for filtering and retrieving data.
Section 2: Analyzing Twitter Data
In this section, readers will learn how to use TextBlob to analyze the sentiment of tweets. It provides an overview of the library’s functionality and demonstrates how to extract sentiment scores from tweet text.
Section 3: Visualizing Twitter Data
Using Matplotlib, this section guides readers through creating various visualizations to represent the collected data. It includes examples of bar charts, pie charts, and scatter plots to illustrate key insights and patterns.
Conclusion
The conclusion summarizes the key steps involved in analyzing and visualizing Twitter data using Python. It emphasizes the power of these tools for understanding public sentiment, tracking brand performance, and conducting market research.
