# WASSA-2017 Shared Task on Emotion Intensity

arXiv: https://arxiv.org/abs/1708.03700 · HTML: https://mlsanity.com/paper/1708.03700/

## Key facts

- Cited by 38 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2017.

## Built on by (most cited first, 30 of 38)

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- [A Wide Evaluation of ChatGPT on Affective Computing Tasks](https://mlsanity.com/paper/2308.13911/) (2023)
- [BLEU, METEOR, BERTScore: Evaluation of Metrics Performance in Assessing Critical Translation Errors in Sentiment-oriented Text](https://mlsanity.com/paper/2109.14250/) (2021)
- [Knowledge Fusion By Evolving Weights of Language Models](https://mlsanity.com/paper/2406.12208/) (2024)
- [Multi-Task Learning with Sentiment, Emotion, and Target Detection to Recognize Hate Speech and Offensive Language](https://mlsanity.com/paper/2109.10255/) (2021)
- [IterIS: Iterative Inference-Solving Alignment for LoRA Merging](https://mlsanity.com/paper/2411.15231/) (2024)
- [Emotion Ratings: How Intensity, Annotation Confidence and Agreements are Entangled](https://mlsanity.com/paper/2103.01667/) (2021)
- [Fine-Grained Emotion Prediction by Modeling Emotion Definitions](https://mlsanity.com/paper/2107.12135/) (2021)
- [SemEval-2025 Task 11: Bridging the Gap in Text-Based Emotion Detection](https://mlsanity.com/paper/2503.07269/) (2025)
- [Where are We in Event-centric Emotion Analysis? Bridging Emotion Role Labeling and Appraisal-based Approaches](https://mlsanity.com/paper/2309.02092/) (2023)
- [Language Models Predict Empathy Gaps Between Social In-groups and Out-groups](https://mlsanity.com/paper/2503.01030/) (2025)
- [Out-of-the-Box Conditional Text Embeddings from Large Language Models](https://mlsanity.com/paper/2504.16411/) (2025)
- [Emotions in the Loop: A Survey of Affective Computing for Emotional Support](https://mlsanity.com/paper/2505.01542/) (2025)
- [Challenges in Translation of Emotions in Multilingual User-Generated Content: Twitter as a Case Study](https://mlsanity.com/paper/2106.10719/) (2021)
- [Sentiment-Aware Measure (SAM) for Evaluating Sentiment Transfer by Machine Translation Systems](https://mlsanity.com/paper/2109.14895/) (2021)
- [Please, Don't Forget the Difference and the Confidence Interval when Seeking for the State-of-the-Art Status](https://mlsanity.com/paper/2205.11134/) (2022)
- ["splink" is happy and "phrouth" is scary: Emotion Intensity Analysis for Nonsense Words](https://mlsanity.com/paper/2202.12132/) (2022)
- [Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus](https://mlsanity.com/paper/2210.06405/) (2022)
- [Modelling Emotion Dynamics in Song Lyrics with State Space Models](https://mlsanity.com/paper/2210.09434/) (2022)
- [Silhouettes and quasi residual plots for neural nets and tree-based classifiers](https://mlsanity.com/paper/2106.08814/) (2021)
- [Emotion-Oriented Behavior Model Using Deep Learning](https://mlsanity.com/paper/2311.14674/) (2023)
- [EFO: the Emotion Frame Ontology](https://mlsanity.com/paper/2401.10751/) (2024)
- [Predicting Emotion Intensity in Polish Political Texts: Comparing Supervised Models and Large Language Models in a Resource-Poor Language](https://mlsanity.com/paper/2407.12141/) (2024)
- [Focusing Condition: Inference-Time Self-Contrastive Steering Elicits Better Conditional Text Embeddings in LLMs](https://mlsanity.com/paper/2609.32684/) (2026)
- [Fear Is Not Always Fear: Variation in Emotion Label Equivalence Across Datasets](https://mlsanity.com/paper/2610.12841/) (2026)
- [Guilt by Association: Emotion Intensities in Lexical Representations](https://mlsanity.com/paper/2104.08679/) (2021)
- [Identifying negativity factors from social media text corpus using sentiment analysis method](https://mlsanity.com/paper/2107.02175/) (2021)

## Abstract (arXiv)

We present the first shared task on detecting the intensity of emotion felt by the speaker of a tweet. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities using a technique called best--worst scaling (BWS). We show that the annotations lead to reliable fine-grained intensity scores (rankings of tweets by intensity). The data was partitioned into training, development, and test sets for the competition. Twenty-two teams participated in the shared task, with the best system obtaining a Pearson correlation of 0.747 with the gold intensity scores. We summarize the machine learning setups, resources, and tools used by the participating teams, with a focus on the techniques and resources that are particularly useful for the task. The emotion intensity dataset and the shared task are helping improve our understanding of how we convey more or less intense emotions through language.