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

arXiv: https://arxiv.org/abs/2108.13898 · HTML: https://mlsanity.com/paper/2108.13898/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 22 references that are in the map.
- First posted on arXiv 31 Aug 2021.

## Builds on (most cited first, 7 of 7)

- [SemEval-2017 Task 4: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.00741/) (2019)
- [Beneath the Tip of the Iceberg: Current Challenges and New Directions in Sentiment Analysis Research](https://mlsanity.com/paper/2005.00357/) (2020)
- [Stance and Sentiment in Tweets](https://mlsanity.com/paper/1605.01655/) (2016)
- [A Fine-Grained Sentiment Dataset for Norwegian](https://mlsanity.com/paper/1911.12722/) (2019)
- [Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose](https://mlsanity.com/paper/1306.5204/) (2013)
- [A Longitudinal Assessment of the Persistence of Twitter Datasets](https://mlsanity.com/paper/1709.09186/) (2017)
- [Opinions are Made to be Changed: Temporally Adaptive Stance Classification](https://mlsanity.com/paper/2108.12476/) (2021)

## Built on by (most cited first, 2 of 2)

- [Building for Tomorrow: Assessing the Temporal Persistence of Text Classifiers](https://mlsanity.com/paper/2205.05435/) (2022)
- [Capturing Stance Dynamics in Social Media: Open Challenges and Research Directions](https://mlsanity.com/paper/2109.00475/) (2021)

## Abstract (arXiv)

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.