Emoji-Based Transfer Learning for Sentiment Tasks
This work addresses sentiment analysis and hate speech detection for low-resource languages, but it is incremental as it applies an existing transfer learning method to a new data source (emojis).
The paper tackled low-resource sentiment tasks in non-English languages by using emoji-based transfer learning, achieving an F1 score improvement of up to +0.280 over the baseline when using monolingual source tasks.
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.