# Emotion Intensities in Tweets

arXiv: https://arxiv.org/abs/1708.03696 · HTML: https://mlsanity.com/paper/1708.03696/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Aug 2017.

## Built on by (most cited first, 30 of 32)

- [Fusing finetuned models for better pretraining](https://mlsanity.com/paper/2204.03044/) (2022)
- [Knowledge is a Region in Weight Space for Fine-tuned Language Models](https://mlsanity.com/paper/2302.04863/) (2023)
- [ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning](https://mlsanity.com/paper/2212.01378/) (2022)
- [SpanEmo: Casting Multi-label Emotion Classification as Span-prediction](https://mlsanity.com/paper/2101.10038/) (2021)
- [Ruddit: Norms of Offensiveness for English Reddit Comments](https://mlsanity.com/paper/2106.05664/) (2021)
- [Where to start? Analyzing the potential value of intermediate models](https://mlsanity.com/paper/2211.00107/) (2022)
- [''Fifty Shades of Bias'': Normative Ratings of Gender Bias in GPT Generated English Text](https://mlsanity.com/paper/2310.17428/) (2023)
- [Natural Language Inference Prompts for Zero-shot Emotion Classification in Text across Corpora](https://mlsanity.com/paper/2209.06701/) (2022)
- [ExpCLIP: Bridging Text and Facial Expressions via Semantic Alignment](https://mlsanity.com/paper/2308.14448/) (2023)
- [Multi-Task Learning with Sentiment, Emotion, and Target Detection to Recognize Hate Speech and Offensive Language](https://mlsanity.com/paper/2109.10255/) (2021)
- [Supporting Cognitive and Emotional Empathic Writing of Students](https://mlsanity.com/paper/2105.14815/) (2021)
- [English Prompts are Better for NLI-based Zero-Shot Emotion Classification than Target-Language Prompts](https://mlsanity.com/paper/2402.03223/) (2024)
- [Language Models Predict Empathy Gaps Between Social In-groups and Out-groups](https://mlsanity.com/paper/2503.01030/) (2025)
- [Emotion Stimulus Detection in German News Headlines](https://mlsanity.com/paper/2107.12920/) (2021)
- [Building a Dialogue Corpus Annotated with Expressed and Experienced Emotions](https://mlsanity.com/paper/2205.11867/) (2022)
- [Can Third-parties Read Our Emotions?](https://mlsanity.com/paper/2504.18673/) (2025)
- [SemEval-2026 Task 3: Dimensional Aspect-Based Sentiment Analysis (DimABSA)](https://mlsanity.com/paper/2604.07066/) (2026)
- ["You are an expert annotator": Automatic Best-Worst-Scaling Annotations for Emotion Intensity Modeling](https://mlsanity.com/paper/2403.17612/) (2024)
- [EmoDNN: Understanding emotions from short texts through a deep neural network ensemble](https://mlsanity.com/paper/2106.01706/) (2021)
- ["splink" is happy and "phrouth" is scary: Emotion Intensity Analysis for Nonsense Words](https://mlsanity.com/paper/2202.12132/) (2022)
- [Expressions Causing Differences in Emotion Recognition in Social Networking Service Documents](https://mlsanity.com/paper/2208.14244/) (2022)
- [Automatic Emotion Experiencer Recognition](https://mlsanity.com/paper/2305.16731/) (2023)
- [Deep Model Compression Also Helps Models Capture Ambiguity](https://mlsanity.com/paper/2306.07061/) (2023)
- [Self-Knowledge Distillation for Learning Ambiguity](https://mlsanity.com/paper/2406.09719/) (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)
- [Understanding Mental Health Content on Social Media and Its Effect Towards Suicidal Ideation](https://mlsanity.com/paper/2501.09309/) (2025)
- [DimABSA: Building Multilingual and Multidomain Datasets for Dimensional Aspect-Based Sentiment Analysis](https://mlsanity.com/paper/2601.23022/) (2026)
- [Quality and Agreement in Multilabel Emotion Annotation: A Case Study and Evaluation Framework](https://mlsanity.com/paper/2606.21069/) (2026)
- [An Analytical Emotion Framework of Rumour Threads on Social Media](https://mlsanity.com/paper/2502.16560/) (2025)
- [Reasoning under Ambiguity: Uncertainty-Aware Multilingual Emotion Classification under Partial Supervision](https://mlsanity.com/paper/2602.05471/) (2026)

## Abstract (arXiv)

This paper examines the task of detecting intensity of emotion from text. We create the first datasets of tweets annotated for anger, fear, joy, and sadness intensities. We use a technique called best--worst scaling (BWS) that improves annotation consistency and obtains reliable fine-grained scores. We show that emotion-word hashtags often impact emotion intensity, usually conveying a more intense emotion. Finally, we create a benchmark regression system and conduct experiments to determine: which features are useful for detecting emotion intensity, and, the extent to which two emotions are similar in terms of how they manifest in language.