Organisations which sell services or products to mass audiences struggle to understand the reasons why their customers are happy, and especially how the preferences evolve over time. The problem of understanding reasons for customer satisfaction over time stems from methodological difficulty in analysing written customer reviews as time series data. This project trials use of sentiment analysis to understand evolution of feedback over time. Sentiment models trained using Recurrent Neural Network, Naive Bayes and Maximum Entropy are compared and the best model is selected to predict feedback in the future. Difference in predictive accuracy over time is assessed for the selected model. Moreover, visuals are developed to depict how text features and themes vary in importance when it comes to accurate prediction of satisfaction over time. The objective is to enable real-time visualization and understanding of patterns in customer feedback over time from big text corpora