Twitter Sentiment Analysis Machine Learning Project | Live Twitter API | NLP Series | Project#9

In this tutorial, we are performing sentiment analysis on live tweets pulled from Twitter via API using Python Tweepy. We are applying Python Library TextBlob & Natural Language Toolkit, viz, NLTK Sentiment Vader SentimentIntensityAnalyzer for Sentiment Computations.. 🔥 Links to the entire series: #1 - How to set up your own Twitter API, that enables you to pull 2 million tweets per month: youtu.be/fQLa40L_BWA #2 - A quick walkthrough to Tweepy, which is an easy-to-use Python library for accessing Twitter API: youtu.be/9ueHyvMOqJ4 #3: Sentiment Analysis of Live Tweets: youtu.be/YdRTs0LmiuU Happy learning :) 🔥 Sections 00:00 Introduction 01:07 Solution Architecture 02:16 TextBlob - Basics 03:58 NLTK Sentiment Vader - Basics 05:07 Sentiment Analysis on Tweets 08:12 Donut Chart & Word Cloud 11:16 Let's talk Machine Learning 🔥 Resources: Project files: https://drive.google.com/drive/folders/1ya2UGUuTjE_YmNv9kw6F3vP-Cd-Up7H7?usp=sharing Tweepy Documentation: https://docs.tweepy.org/en/stable/index.html Twitter API 2.0 Documentation: https://developer.twitter.com/en/docs/twitter-api Twitter OAuth Comparison: https://developer.twitter.com/en/docs/authentication/guides/v2-authentication-mapping TextBlob Sentiments Documentation: https://textblob.readthedocs.io/en/dev/ NLTK Sentiment Vader SentimentIntensityAnalyzer Documentation: https://www.nltk.org/api/nltk.sentiment.vader.html Our other popular ML Projects: 1. Sentiment Analysis Project using LSTM: https://youtu.be/oWo9SNcyxlI 2. Sentiment Analysis Project (End-to-end) with ML Model Building + Deployment (using Flask): ---- a. Model Building: https://youtu.be/lKAdxN0qrgk (Part-1) ---- b. Model Deployment: https://youtu.be/KEQCVwJU5KU (Part-2) 3. Sentiment Analysis Project using Traditional ML: https://youtu.be/zwR6M5zpnWs 4. Analytics-enabled Marketing: youtu.be/g7hEPopJ4MY 5. Credit Scoring Project: youtu.be/8jzvzRo3Ij0 6. Face Recognition Project: youtu.be/4EeUkpAYrYo 🔥 Do like, share & subscribe to our channel. Keep in touch: Website: https://www.skillcate.com Email: [email protected] Medium: https://medium.com/@skillcate GitHub: https://github.com/skillcate Facebook: https://www.facebook.com/groups/mlprojects Instagram: https://www.instagram.com/skillcate/