The purpose of this project is to compute the sentiment of text information - in my case, tweets posted in 2015 regarding US airlines - and answer the research question: “What can public opinion on Twitter tell us about the US airlines in 2015?” The goal is to essentially use sentiment analysis on Twitter data to get insight into the people’s opinions on US airlines. Central to sentiment analysis are techniques first developed in text mining. Some of those techniques require a large collection of classified text data often divided into two types of data, a training data set and a testing data set. The training data set is further divided into data used solely for the purpose of building the model and data used for validating the model. The process of building a model is iterative, with the model being successively refined until an acceptable performance is achieved. The model is then used on the testing data in order to calculate its performance characteristics.