Sentiment and emotion analysis technologies have quickly gained momentum in industry and academia. This popularity has spawned a myriad of service and tools. Due to the lack of common interfaces and models, each of these services imposes specific interfaces and representation models. Heterogeneity makes it costly to integrate different services, evaluate them or switch between them. This work aims to remedy heterogeneity by providing an extensible framework and an API aligned with the NLP Interchange Format service specification. It also includes a reference implementation, a first step towards a successful and cost-effective adoption. The specific contributions in this paper are: (i) the Senpy framework; (ii) an architecture for the framework that follows a plug-in approach; (iii) a reference open source implementation of the architecture; (iv) the use and validation of the framework and architecture in a big data sentiment analysis European project. Our aim is to foster the development of a new generation of emotion aware services by isolating the development of new algorithms from the representation of results and the deployment of services.