  Hashtag segmentation, also known as hashtag decomposition, is a common step
in preprocessing pipelines for social media datasets. It usually precedes tasks
such as sentiment analysis and hate speech detection. For sentiment analysis in
medium to low-resourced languages, previous research has demonstrated that a
multilingual approach that resorts to machine translation can be competitive or
superior to previous approaches to the task. We develop a zero-shot hashtag
segmentation framework and demonstrate how it can be used to improve the
accuracy of multilingual sentiment analysis pipelines. Our zero-shot framework
establishes a new state-of-the-art for hashtag segmentation datasets,
surpassing even previous approaches that relied on feature engineering and
language models trained on in-domain data.
