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
- Basic Python knowledge (I explain each step so you can understand what I am doing)
Learn how to connect and download tweets through Twitter API. From there I will show you how to clean this data and prepare them for sentiment analysis. There are two most commonly used approaches to sentiment analysis so we will look at both of them. First one is Lexicon based approach where you can use prepared lexicons to analyse data and get sentiment of given text. Second one is Machine learning approach where we train our own model on labeled data and then we show it new data and hopefully our model will show us sentiment. At the end you will be able to build your own script to analyze sentiment of hundreds or even thousands of tweets about topic you choose.
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
I have master degree in civil engineering but since college I have strong passion about markets. I trade Forex and Futures since 2013 and later I added Crypto as well. Coding is not my main focus but I like to see backtesting results of my strategies before I add them to my portfolio. That is why I started to learn Python as a tool to help me with this. I spent countless hours developing my skills on trading and now I want to help another traders to use some of my knowledge. I am sure everyone will find some use of informations and tips that I provide.