  Sentiment tasks such as hate speech detection and sentiment analysis,
especially when performed on languages other than English, are often
low-resource. In this study, we exploit the emotional information encoded in
emojis to enhance the performance on a variety of sentiment tasks. This is done
using a transfer learning approach, where the parameters learned by an
emoji-based source task are transferred to a sentiment target task. We analyse
the efficacy of the transfer under three conditions, i.e. i) the emoji content
and ii) label distribution of the target task as well as iii) the difference
between monolingually and multilingually learned source tasks. We find i.a.
that the transfer is most beneficial if the target task is balanced with high
emoji content. Monolingually learned source tasks have the benefit of taking
into account the culturally specific use of emojis and gain up to F1 +0.280
over the baseline.
