Abstract
Hashtags are often employed on social media and beyond to add metadata to a textual utterance with the goal of increasing discoverability, aiding search, or providing additional semantics. However, the semantic content of hashtags is not straightforward to infer
as these represent ad-hoc conventions which
frequently include multiple words joined together and can include abbreviations and unorthodox spellings. We build a dataset of
12,594 hashtags split into individual segments
and propose a set of approaches for hashtag segmentation by framing it as a pairwise
ranking problem between candidate segmentations.1 Our novel neural approaches demonstrate 24.6% error reduction in hashtag segmentation accuracy compared to the current
state-of-the-art method. Finally, we demonstrate that a deeper understanding of hashtag semantics obtained through segmentation
is useful for downstream applications such as
sentiment analysis, for which we achieved a
2.6% increase in average recall on the SemEval 2017 sentiment analysis dataset