Machine learning and Lexicon approach to Sentiment analysis
Machine learning and Lexicon approach to Sentiment analysis
Video: .mp4 (1280x720, 30 fps(r)) | Audio: aac, 48000 Hz, 2ch | Size: 1.53 GB
Genre: eLearning Video | Duration: 23 lectures (3 hour, 25 mins) | Language: English
Machine learning and Lexicon based approach to Sentiment analysis using Twitter API
Video: .mp4 (1280x720, 30 fps(r)) | Audio: aac, 48000 Hz, 2ch | Size: 1.53 GB
Genre: eLearning Video | Duration: 23 lectures (3 hour, 25 mins) | Language: English
Machine learning and Lexicon based approach to Sentiment analysis using Twitter API
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
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
Machine learning and Lexicon approach to Sentiment analysis
