Hello guys and welcome to this 4th part of this series on Twitter sentiment analysis using NLTK. In the previous parts, we learn how to create the dataset for predicting and we also predict some reviews, in this tutorial, we will load some tweets from Tweeter and then predict the nature of tweets.

Hello and welcome to the 3rd part of this series on Twitter Sentiment Analysis using NLTK. In the previous parts we learned about the basics of NLTK and then creating a dataset using positive and negative movie reviews, In this part, we will create a function to predict the nature of sentences and later we will use it for tweets. So let's understand how it works.

Hello and welcome to part 2 of this series, In part 1 we learned the basics of NLTK like, tokenizing, stop words, part of speech tagging etc. In this part our work is easy. All we have to do is to load positive and negative movie reviews, split them into words and then process it in such a way that efficiency will increase, after the completion of the process we will pickle it so that we don't need to process it again and again.

In this tutorial series, we will learn about various features of NLTK, even though there is a lot to learn in NLTK, we will learn some basic here. We will also build a project on Twitter Sentiment Analysis, in which we will load various tweets on a particular subject and then try to analyse if the tweet tends to positive or negative, and then we will see the whole analysis on the live graph.