  Adjective phrases like "a little bit surprised", "completely shocked", or
"not stunned at all" are not handled properly by currently published
state-of-the-art emotion classification and intensity prediction systems which
use pre-dominantly non-contextualized word embeddings as input. Based on this
finding, we analyze differences between embeddings used by these systems in
regard to their capability of handling such cases. Furthermore, we argue that
intensifiers in context of emotion words need special treatment, as is
established for sentiment polarity classification, but not for more
fine-grained emotion prediction. To resolve this issue, we analyze different
aspects of a post-processing pipeline which enriches the word representations
of such phrases. This includes expansion of semantic spaces at the phrase level
and sub-word level followed by retrofitting to emotion lexica. We evaluate the
impact of these steps with A La Carte and Bag-of-Substrings extensions based on
pretrained GloVe, Word2vec, and fastText embeddings against a crowd-sourced
corpus of intensity annotations for tweets containing our focus phrases. We
show that the fastText-based models do not gain from handling these specific
phrases under inspection. For Word2vec embeddings, we show that our
post-processing pipeline improves the results by up to 8% on a novel dataset
densely populated with intensifiers.
