# SemEval-2015 Task 10: Sentiment Analysis in Twitter

arXiv: https://arxiv.org/abs/1912.02387 · HTML: https://mlsanity.com/paper/1912.02387/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2019.

## Built on by (most cited first, 16 of 16)

- [SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)](https://mlsanity.com/paper/2304.06845/) (2023)
- [Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis](https://mlsanity.com/paper/2109.08256/) (2021)
- [Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction](https://mlsanity.com/paper/2106.09790/) (2021)
- [SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis (DimABSA)](https://mlsanity.com/paper/2604.07066/) (2026)
- [How Will Your Tweet Be Received? Predicting the Sentiment Polarity of Tweet Replies](https://mlsanity.com/paper/2104.10513/) (2021)
- [Learning to Play Chess from Textbooks (LEAP): a Corpus for Evaluating Chess Moves based on Sentiment Analysis](https://mlsanity.com/paper/2310.20260/) (2023)
- [Non-Compositionality in Sentiment: New Data and Analyses](https://mlsanity.com/paper/2310.20656/) (2023)
- [Sentiment Analysis in Twitter for Macedonian](https://mlsanity.com/paper/2109.13725/) (2021)
- [Dictionary-Assisted Supervised Contrastive Learning](https://mlsanity.com/paper/2210.15172/) (2022)
- [Natural Language Processing for Policymaking](https://mlsanity.com/paper/2302.03490/) (2023)
- [Optimal strategies to perform multilingual analysis of social content for a novel dataset in the tourism domain](https://mlsanity.com/paper/2311.14727/) (2023)
- [From Adoption to Adaption: Tracing the Diffusion of New Emojis on Twitter](https://mlsanity.com/paper/2402.14187/) (2024)
- [DimABSA: Building Multilingual and Multidomain Datasets for Dimensional Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2601.23022/) (2026)
- [LSTM based models stability in the context of Sentiment Analysis for social media](https://mlsanity.com/paper/2211.11246/) (2022)
- [SemEval-2017 Task 4: Sentiment Analysis in Twitter using BERT](https://mlsanity.com/paper/2401.07944/) (2024)
- [Sentiment Analysis in SemEval: A Review of Sentiment Identification Approaches](https://mlsanity.com/paper/2503.10457/) (2025)

## Abstract (arXiv)

In this paper, we describe the 2015 iteration of the SemEval shared task on Sentiment Analysis in Twitter. This was the most popular sentiment analysis shared task to date with more than 40 teams participating in each of the last three years. This year's shared task competition consisted of five sentiment prediction subtasks. Two were reruns from previous years: (A) sentiment expressed by a phrase in the context of a tweet, and (B) overall sentiment of a tweet. We further included three new subtasks asking to predict (C) the sentiment towards a topic in a single tweet, (D) the overall sentiment towards a topic in a set of tweets, and (E) the degree of prior polarity of a phrase.