Mining Opinion Based on Tweets about Student Exchange with Tweepy and TextBlob

To find out people's opinions about student exchange, this research was made to find out by taking a data set from Twitter with a machine learning calculation within the form of Tweepy and after that handling it with TextBlob. TextBlob is one of the libraries in python 2 and python 3 which is used to process textual data. After getting the results, it is expected to use the data to help the community know what student exchange is and its benefits. Social media (Facebook, Instagram, Twitter, etc.) these days can be utilized for analysing the objects, e.g. political sees, items, administrations, etc. Social media is widely used at this time and we choose twitter because twitter is a social media application that is popular in the world and indeed a lot of research using twitter. Twitter had become the third-highest social organization in January 2009 from its previous position of twenty-two. Currently, more than 500 million selected clients in 2012, generating more than 340 million tweets. The conclusion is that there are still many people who do not know what student exchange is as much as 22% and 54% consider student exchange to be negative because they do not know the benefits of student exchange. Positive knowledge about student exchange is only 24%, this will support further research of creating a knowledge management system on student exchange. So hopefully that many people are interested in joining the student exchange program.