We investigated how different Natural Language Processing (NLP) techniques could be used to perform sentiment analysis on real user generated text data from the Sentiment140 dataset [1]. First we investigated an LSTM model before deciding on using the self-attention network code from [2] because of the possible speed and accuracy advantages.