Online Social Networks (OSNs) have emerged as a global media for forming and shaping opinions on a broad spectrum of topics like politics, e-commerce, sports, etc. So, research on understanding and predicting opinion dynamics in OSNs, especially using a tractable linear model, has abound in literature. However, these linear models are too simple to uncover the actual complex dynamics of opinion flow in social networks. In this paper, we propose SLANT+, a novel nonlinear generative model for opinion dynamics, by extending our earlier linear opinion model SLANT [7]. To design this model, we rely on a network-guided recurrent neural network architecture which learns a proper temporal representation of the messages as well as the underlying network. Furthermore, we probe various signals from the real life datasets and offer a conceptually interpretable nonlinear function that not only provides concrete clues of the opinion exchange process, but also captures the coupled dynamics of message timings and opinion flow. As a result, with five real-life datasets crawled from Twitter, our proposal gives significant accuracy boost over six state-of-the-art baselines.