Machine learning and Lexicon approach to Sentiment analysis
Machine learning and Lexicon based approach to Sentiment analysis using Twitter API
Ratings 3.89 / 5.00
What You Will 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
Description
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 Should Attend!
- Beginner Python developers curious about data science
- Anyone who is interested in data analysis
- People who wants to include sentiment analysis for their projects
Tags
- Machine Learning
- Python
- Sentiment Analysis
Subscribers
5984
Lectures
23
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