  Social media, as a means for computer-mediated communication, has been
extensively used to study the sentiment expressed by users around events or
topics. There is however a gap in the longitudinal study of how sentiment
evolved in social media over the years. To fill this gap, we develop TM-Senti,
a new large-scale, distantly supervised Twitter sentiment dataset with over 184
million tweets and covering a time period of over seven years. We describe and
assess our methodology to put together a large-scale, emoticon- and emoji-based
labelled sentiment analysis dataset, along with an analysis of the resulting
dataset. Our analysis highlights interesting temporal changes, among others in
the increasing use of emojis over emoticons. We publicly release the dataset
for further research in tasks including sentiment analysis and text
classification of tweets. The dataset can be fully rehydrated including tweet
metadata and without missing tweets thanks to the archive of tweets publicly
available on the Internet Archive, which the dataset is based on.
