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Because of license restrictions, it often becomes impossible to strictlyreproduce most research results on Twitter data already a few months after thecreation of the corpus. This situation worsened gradually as time passes andtweets become inaccessible. This is a critical issue for reproducible andaccountable research on social media. We partly solve this challenge byannotating a new Twitter-like corpus from an alternative large social mediumwith licenses that are compatible with reproducible experiments: Mastodon. Wemanually annotate both dialogues and sentiments on this corpus, and train amulti-task hierarchical recurrent network on joint sentiment and dialog actrecognition. We experimentally demonstrate that transfer learning may beefficiently achieved between both tasks, and further analyze some specificcorrelations between sentiments and dialogues on social media. Both theannotated corpus and deep network are released with an open-source license.

Multi-task dialog act and sentiment recognition on Mastodon