  A short and simple text carrying no emotion can represent some strong
emotions when reading along with its context, i.e., the same sentence can
express extreme anger as well as happiness depending on its context. In this
paper, we propose a Contextual Affect Detection (CAD) framework which learns
the inter-dependence of words in a sentence, and at the same time the
inter-dependence of sentences in a dialogue. Our proposed CAD framework is
based on a Gated Recurrent Unit (GRU), which is further assisted by contextual
word embeddings and other diverse hand-crafted feature sets. Evaluation and
analysis suggest that our model outperforms the state-of-the-art methods by
5.49% and 9.14% on Friends and EmotionPush dataset, respectively.
