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Social media, as a means for computer-mediated communication, has beenextensively used to study the sentiment expressed by users around events ortopics. There is however a gap in the longitudinal study of how sentimentevolved 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 184million tweets and covering a time period of over seven years. We describe andassess our methodology to put together a large-scale, emoticon- and emoji-basedlabelled sentiment analysis dataset, along with an analysis of the resultingdataset. Our analysis highlights interesting temporal changes, among others inthe increasing use of emojis over emoticons. We publicly release the datasetfor further research in tasks including sentiment analysis and textclassification of tweets. The dataset can be fully rehydrated including tweetmetadata and without missing tweets thanks to the archive of tweets publiclyavailable on the Internet Archive, which the dataset is based on.

The emojification of sentiment on social media: Collection and analysis of a longitudinal Twitter sentiment dataset