# SemEval-2014 Task 9: Sentiment Analysis in Twitter

arXiv: https://arxiv.org/abs/1912.02990 · HTML: https://mlsanity.com/paper/1912.02990/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2019.

## Built on by (most cited first, 20 of 20)

- [A Close Look into the Calibration of Pre-trained Language Models](https://mlsanity.com/paper/2211.00151/) (2022)
- [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)
- [Evaluating Emotion Arcs Across Languages: Bridging the Global Divide in Sentiment Analysis](https://mlsanity.com/paper/2306.02213/) (2023)
- [Disentangling Representations of Text by Masking Transformers](https://mlsanity.com/paper/2104.07155/) (2021)
- [Frustratingly Easy Sentiment Analysis of Text Streams: Generating High-Quality Emotion Arcs Using Emotion Lexicons](https://mlsanity.com/paper/2210.07381/) (2022)
- [How Will Your Tweet Be Received? Predicting the Sentiment Polarity of Tweet Replies](https://mlsanity.com/paper/2104.10513/) (2021)
- [Towards Target-dependent Sentiment Classification in News Articles](https://mlsanity.com/paper/2105.09660/) (2021)
- [A Survey on Automated Sarcasm Detection on Twitter](https://mlsanity.com/paper/2202.02516/) (2022)
- [Hashtag-Guided Low-Resource Tweet Classification](https://mlsanity.com/paper/2302.10143/) (2023)
- [Structured Probabilistic Coding](https://mlsanity.com/paper/2312.13933/) (2023)
- [A Robust Deep Ensemble Classifier for Figurative Language Detection](https://mlsanity.com/paper/2107.04372/) (2021)
- [Sentiment Analysis in Twitter for Macedonian](https://mlsanity.com/paper/2109.13725/) (2021)
- [An LSTM model for Twitter Sentiment Analysis](https://mlsanity.com/paper/2212.01791/) (2022)
- [Optimal strategies to perform multilingual analysis of social content for a novel dataset in the tourism domain](https://mlsanity.com/paper/2311.14727/) (2023)
- [SASICM A Multi-Task Benchmark For Subtext Recognition](https://mlsanity.com/paper/2106.06944/) (2021)
- [Plumeria at SemEval-2022 Task 6: Robust Approaches for Sarcasm Detection for English and Arabic Using Transformers and Data Augmentation](https://mlsanity.com/paper/2203.04111/) (2022)
- [CS-UM6P at SemEval-2022 Task 6: Transformer-based Models for Intended Sarcasm Detection in English and Arabic](https://mlsanity.com/paper/2206.08415/) (2022)
- [LSTM based models stability in the context of Sentiment Analysis for social media](https://mlsanity.com/paper/2211.11246/) (2022)
- [Sentiment Analysis in SemEval: A Review of Sentiment Identification Approaches](https://mlsanity.com/paper/2503.10457/) (2025)

## Abstract (arXiv)

We describe the Sentiment Analysis in Twitter task, ran as part of SemEval-2014. It is a continuation of the last year's task that ran successfully as part of SemEval-2013. As in 2013, this was the most popular SemEval task; a total of 46 teams contributed 27 submissions for subtask A (21 teams) and 50 submissions for subtask B (44 teams). This year, we introduced three new test sets: (i) regular tweets, (ii) sarcastic tweets, and (iii) LiveJournal sentences. We further tested on (iv) 2013 tweets, and (v) 2013 SMS messages. The highest F1-score on (i) was achieved by NRC-Canada at 86.63 for subtask A and by TeamX at 70.96 for subtask B.