  Human verbal communication includes affective messages which are conveyed
through use of emotionally colored words. There has been a lot of research in
this direction but the problem of integrating state-of-the-art neural language
models with affective information remains an area ripe for exploration. In this
paper, we propose an extension to an LSTM (Long Short-Term Memory) language
model for generating conversational text, conditioned on affect categories. Our
proposed model, Affect-LM enables us to customize the degree of emotional
content in generated sentences through an additional design parameter.
Perception studies conducted using Amazon Mechanical Turk show that Affect-LM
generates naturally looking emotional sentences without sacrificing grammatical
correctness. Affect-LM also learns affect-discriminative word representations,
and perplexity experiments show that additional affective information in
conversational text can improve language model prediction.
