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Multi-Task Dialog Act and Sentiment Recognition on Mastodon
Last updated
Jul 11, 2023
| Key | Value |
|---|
| Author | Cerisara et. al |
| Year | 2018 |
| PDF | Link |
- Release of 800k annotated posts (dialogue and sentiment) from single Mastodon instance.
- Show limited transfer learning possible between both tasks though not correlated globally
# Abstract
- Twitter data not suitable for reproducing research
- Dataset: Annotated dialogues and sentiments on Mastodon
- Train RNN on sentiment and dialog recognition
# Introduction
- Issue: Reproducibility when working on Twitter data
- license restrictions: cannot store tweets in a database for a long period
- proportion of tweets are continuously deleted
- Solution: Use Mastodon data
- user-generated content typically follows CC licence
- Comparison: Mastodon
- decentralized SN
- more characters per post
- mostly composed of people who are technical, disappointed by Twitter, changes of policies, privacy threads, belonging to minority group
- more international (most servers located in Japan and in the West of Europe)
- less developed than Twitter, but growing and useful for research
- Focus of paper: Dialog acts and sentiment recognition
- Provide new Mastodon corpus
- With annotated sentiment
- Analyze dialog acts (speech acts)
- Show correlation between sentiment and dialog acts (via RNN transfer learning)