  Because of license restrictions, it often becomes impossible to strictly
reproduce most research results on Twitter data already a few months after the
creation of the corpus. This situation worsened gradually as time passes and
tweets become inaccessible. This is a critical issue for reproducible and
accountable research on social media. We partly solve this challenge by
annotating a new Twitter-like corpus from an alternative large social medium
with licenses that are compatible with reproducible experiments: Mastodon. We
manually annotate both dialogues and sentiments on this corpus, and train a
multi-task hierarchical recurrent network on joint sentiment and dialog act
recognition. We experimentally demonstrate that transfer learning may be
efficiently achieved between both tasks, and further analyze some specific
correlations between sentiments and dialogues on social media. Both the
annotated corpus and deep network are released with an open-source license.
