# SemEval-2013 Task 2: Sentiment Analysis in Twitter

arXiv: https://arxiv.org/abs/1912.06806 · HTML: https://mlsanity.com/paper/1912.06806/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2019.

## Built on by (most cited first, 22 of 22)

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- [NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis](https://mlsanity.com/paper/2201.08277/) (2022)
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- [The Sentiment Problem: A Critical Survey towards Deconstructing Sentiment Analysis](https://mlsanity.com/paper/2310.12318/) (2023)
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- [Synthetic Lyrics Detection Across Languages and Genres](https://mlsanity.com/paper/2406.15231/) (2024)
- [How Will Your Tweet Be Received? Predicting the Sentiment Polarity of Tweet Replies](https://mlsanity.com/paper/2104.10513/) (2021)
- [Investigating the Impact of Model Instability on Explanations and Uncertainty](https://mlsanity.com/paper/2402.13006/) (2024)
- [Double Entendre: Robust Audio-Based AI-Generated Lyrics Detection via Multi-View Fusion](https://mlsanity.com/paper/2506.15981/) (2025)
- [Modeling the Q-Diversity in a Min-max Play Game for Robust Optimization](https://mlsanity.com/paper/2305.12123/) (2023)
- [M2SA: Multimodal and Multilingual Model for Sentiment Analysis of Tweets](https://mlsanity.com/paper/2404.01753/) (2024)
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- [An Estimation of Online Video User Engagement from Features of Continuous Emotions](https://mlsanity.com/paper/2105.01633/) (2021)
- [Sentiment Analysis in Twitter for Macedonian](https://mlsanity.com/paper/2109.13725/) (2021)
- [Optimal strategies to perform multilingual analysis of social content for a novel dataset in the tourism domain](https://mlsanity.com/paper/2311.14727/) (2023)
- [Measuring Linguistic Diversity During COVID-19](https://mlsanity.com/paper/2104.01290/) (2021)
- [Making Pre-trained Language Models both Task-solvers and Self-calibrators](https://mlsanity.com/paper/2307.11316/) (2023)
- [You Shall Know a Tool by the Traces it Leaves: The Predictability of Sentiment Analysis Tools](https://mlsanity.com/paper/2410.14626/) (2024)
- [Sentiment Analysis in SemEval: A Review of Sentiment Identification Approaches](https://mlsanity.com/paper/2503.10457/) (2025)
- [Investigating Large Language Models' Linguistic Abilities for Text Preprocessing](https://mlsanity.com/paper/2510.11482/) (2025)
- [Addressing the Ecological Fallacy in Larger LMs with Human Context](https://mlsanity.com/paper/2603.05928/) (2026)

## Abstract (arXiv)

In recent years, sentiment analysis in social media has attracted a lot of research interest and has been used for a number of applications. Unfortunately, research has been hindered by the lack of suitable datasets, complicating the comparison between approaches. To address this issue, we have proposed SemEval-2013 Task 2: Sentiment Analysis in Twitter, which included two subtasks: A, an expression-level subtask, and B, a message-level subtask. We used crowdsourcing on Amazon Mechanical Turk to label a large Twitter training dataset along with additional test sets of Twitter and SMS messages for both subtasks. All datasets used in the evaluation are released to the research community. The task attracted significant interest and a total of 149 submissions from 44 teams. The best-performing team achieved an F1 of 88.9% and 69% for subtasks A and B, respectively.