Machine learning and Lexicon approach to Sentiment analysis
What you'll learn
- How to create twitter developer account and connect to twitter API
- Download Tweets, clean and store them in to Pandas DataFrame
- Learn about Tokenization, Lemmatization, Stemming and much more
- Perform Sentiment analysis with Vader and TextBlob lexicons
- Learn about Machine learning approach to Sentiment Analysis
- Build and test machine learning models
Requirements
- Basic Python knowledge (I explain each step so you can understand what I am doing)
Description
Unlock the power of Twitter data with this in-depth course on connecting to and downloading tweets through the Twitter API. Whether you’re interested in analyzing social media trends or conducting sentiment analysis for research or business, this course will guide you every step of the way. You’ll start by learning how to access Twitter’s vast data through its API, downloading tweets that are relevant to your chosen topic.
Once you’ve gathered your data, I’ll show you how to clean and preprocess it, transforming raw tweets into structured data that’s ready for analysis. From there, we’ll dive into the fascinating world of sentiment analysis, exploring the two most commonly used approaches. The first is the Lexicon-based approach, where you’ll leverage pre-built lexicons to determine the sentiment of given text quickly and effectively. This method is great for those looking to get started with sentiment analysis without deep machine learning knowledge.
The second approach is more advanced, using Machine Learning to train a custom model on labeled data. Once your model is trained, you’ll apply it to new data, allowing it to predict sentiment with increasing accuracy. By the end of the course, you’ll have built a powerful script capable of analyzing the sentiment of hundreds or even thousands of tweets on any topic you choose. Whether you’re a beginner or looking to expand your data science toolkit, this course will equip you with practical skills and knowledge to perform sentiment analysis on real-world Twitter data.
Who this course is for:
- Beginner Python developers curious about data science
- Anyone who is interested in data analysis
- People who wants to include sentiment analysis for their projects
Instructor
- 4.2 Instructor Rating
- 248 Reviews
- 11,127 Students
- 5 Courses
I hold a Master’s degree in Civil Engineering, but my true passion has always been in the financial markets. Since my college days, I’ve been fascinated by trading, and I’ve been actively trading Forex and Futures since 2013, with Cryptocurrency joining my portfolio shortly after. While coding isn’t my primary focus, I quickly realized the importance of backtesting strategies before adding them to my trading portfolio. This led me to learn Python as a powerful tool to enhance my trading decision-making.
Over the years, I’ve dedicated countless hours to refining my trading skills and developing effective strategies. Now, I’m eager to share the knowledge and insights I’ve gained with other traders. My goal is to help you leverage the tools and techniques that have worked for me, so you can improve your own trading performance. Whether you’re new to trading or experienced in the field, I believe you’ll find valuable information and practical tips in my courses. I’m here to make complex concepts more accessible and to support your journey toward becoming a more confident and successful trader.