Jonathan Day, et al, have a tutorial on using Qubole to build a sentiment analysis model:
This post covers the use of Qubole, Zeppelin, PySpark, and H2O PySparkling to develop a sentiment analysis model capable of providing real-time alerts on customer product reviews. In particular, this model allows users to monitor any natural language text (such as social media posts or Amazon reviews) and receive alerts when customers post extremely nice (high sentiment) or extremely negative (low sentiment) comments about their products.
In addition to introducing the frameworks used, we will also discuss the concepts of embedding spaces, sentiment analysis, deep neural networks, grid search, stop words, data visualization, and data preparation.
Click through for the demo.