  Human communication includes information, opinions, and reactions. Reactions
are often captured by the affective-messages in written as well as verbal
communications. While there has been work in affect modeling and to some extent
affective content generation, the area of affective word distributions in not
well studied. Synsets and lexica capture semantic relationships across words.
These models however lack in encoding affective or emotional word
interpretations. Our proposed model, Aff2Vec provides a method for enriched
word embeddings that are representative of affective interpretations of words.
Aff2Vec outperforms the state--of--the--art in intrinsic word-similarity tasks.
Further, the use of Aff2Vec representations outperforms baseline embeddings in
downstream natural language understanding tasks including sentiment analysis,
personality detection, and frustration prediction.
