Affect LM: A Neural Language Model for Customizable Affective Text Generation
Human verbal communication includes affective messages which are conveyed through use of emotionally colored words. There has been a lot of research effort 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 generation of 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 can generate 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.
ACL