  Human use language not just to convey information but also to express their
inner feelings and mental states. In this work, we adapt the state-of-the-art
language generation models to generate affective (emotional) text. We posit a
model capable of generating affect-driven and topic-focused sentences without
losing grammatical correctness as the affect intensity increases. We propose to
incorporate emotion as prior for the probabilistic state-of-the-art text
generation model such as GPT-2. The model gives a user the flexibility to
control the category and intensity of emotion as well as the topic of the
generated text. Previous attempts at modelling fine-grained emotions fall out
on grammatical correctness at extreme intensities, but our model is resilient
to this and delivers robust results at all intensities. We conduct automated
evaluations and human studies to test the performance of our model and provide
a detailed comparison of the results with other models. In all evaluations, our
model outperforms existing affective text generation models.
