Learn how to perform sentiment analysis on Twitter data using Python. This course will guide you through the process of collecting, cleaning, and analyzing Twitter data to understand public opinion on various topics.
Course Outline
Introduction to Sentiment Analysis
- What is sentiment analysis?
- Importance of sentiment analysis in data science
Collecting Twitter Data
- Using Tweepy to access Twitter API
- Collecting tweets based on keywords or hashtags
Data Cleaning and Preprocessing
- Removing noise and irrelevant information
- Text normalization and tokenization
Sentiment Analysis Techniques
- Using pre-trained models
- Building custom models using machine learning algorithms
Interpreting Results
- Analyzing sentiment scores
- Visualizing sentiment data
Prerequisites
- Basic knowledge of Python programming
- Familiarity with machine learning concepts
Related Resources
For more information on Python for Data Science, check out our Python for Data Science Course.
Data Visualization
To better understand sentiment analysis, let's look at a sample visualization of sentiment scores for a popular topic.
In this course, you will learn how to apply sentiment analysis to Twitter data, providing valuable insights into public opinion. Join us and start your journey into the world of data science!