  Distributed representations of words have shown to be useful to improve the
effectiveness of IR systems in many sub-tasks like query expansion, retrieval
and ranking. Algorithms like word2vec, GloVe and others are also key factors in
many improvements in different NLP tasks. One common issue with such embedding
models is that words like happy and sad appear in similar contexts and hence
are wrongly clustered close in the embedding space. In this paper we leverage
Aff2Vec, a set of word embeddings models which include affect information, in
order to better capture the affect aspect in news text to achieve better
results in information retrieval tasks, also such embeddings are less hit by
the synonym/antonym issue. We evaluate their effectiveness on two IR related
tasks (query expansion and ranking) over the New York Times dataset (TREC-core
'17) comparing them against other word embeddings based models and classic
ranking models.
