# Evaluating Emotion Arcs Across Languages: Bridging the Global Divide in Sentiment Analysis

arXiv: https://arxiv.org/abs/2306.02213 · HTML: https://mlsanity.com/paper/2306.02213/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 39 references that are in the map.
- First posted on arXiv 3 Jun 2023.

## Builds on (most cited first, 12 of 12)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Crowdsourcing a Word-Emotion Association Lexicon](https://mlsanity.com/paper/1308.6297/) (2013)
- [The emotional arcs of stories are dominated by six basic shapes](https://mlsanity.com/paper/1606.07772/) (2016)
- [NaijaSenti: A Nigerian Twitter Sentiment Corpus for Multilingual Sentiment Analysis](https://mlsanity.com/paper/2201.08277/) (2022)
- [SemEval-2023 Task 12: Sentiment Analysis for African Languages (AfriSenti-SemEval)](https://mlsanity.com/paper/2304.06845/) (2023)
- [Best Practices in the Creation and Use of Emotion Lexicons](https://mlsanity.com/paper/2210.07206/) (2022)
- [SemEval-2014 Task 9: Sentiment Analysis in Twitter](https://mlsanity.com/paper/1912.02990/) (2019)
- [Emotion Dynamics in Movie Dialogues](https://mlsanity.com/paper/2103.01345/) (2021)
- [Practical and Ethical Considerations in the Effective use of Emotion and Sentiment Lexicons](https://mlsanity.com/paper/2011.03492/) (2020)
- [From Once Upon a Time to Happily Ever After: Tracking Emotions in Novels and Fairy Tales](https://mlsanity.com/paper/1309.5909/) (2013)

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

- [Charting the Landscape of African NLP: Mapping Progress and Shaping the Road Ahead](https://mlsanity.com/paper/2505.21315/) (2025)
- [The Emotion Dynamics of Literary Novels](https://mlsanity.com/paper/2403.02474/) (2024)
- [The Nature of NLP: Analyzing Contributions in NLP Papers](https://mlsanity.com/paper/2409.19505/) (2024)
- [Language and Mental Health: Measures of Emotion Dynamics from Text as Linguistic Biosocial Markers](https://mlsanity.com/paper/2310.17369/) (2023)
- [Feel the Difference? A Comparative Analysis of Emotional Arcs in Real and LLM-Generated CBT Sessions](https://mlsanity.com/paper/2508.20764/) (2025)
- [WorryWords: Norms of Anxiety Association for over 44k English Words](https://mlsanity.com/paper/2411.03966/) (2024)
- [Emotion Granularity from Text: An Aggregate-Level Indicator of Mental Health](https://mlsanity.com/paper/2403.02281/) (2024)
- [Words of Warmth: Trust and Sociability Norms for over 26k English Words](https://mlsanity.com/paper/2506.03993/) (2025)
- [Affect, Body, Cognition, Demographics, and Emotion: The ABCDE of Text Features for Computational Affective Science](https://mlsanity.com/paper/2512.17752/) (2025)
- [The Language of Interoception: Examining Embodiment and Emotion Through a Corpus of Body Part Mentions](https://mlsanity.com/paper/2505.16189/) (2025)
- [The Shifting Landscape of Vaccine Discourse: Insights From a Decade of Pre- to Post-COVID-19 Vaccine Posts on Social Media](https://mlsanity.com/paper/2511.16832/) (2025)
- [ClaimFlow: Tracing the Evolution of Scientific Claims in NLP](https://mlsanity.com/paper/2603.16073/) (2026)
- [Annotating Dimensions of Social Perception in Text: A Sentence-Level Dataset of Warmth and Competence](https://mlsanity.com/paper/2601.06316/) (2026)
- [When are We Worried? Temporal Trends of Anxiety and What They Reveal about Us](https://mlsanity.com/paper/2602.10400/) (2026)
- [Emotion Profiling in LLM-Based Literary Translation: Systematic Shifts Across MT and Post-Editing](https://mlsanity.com/paper/2606.10113/) (2026)
- [Steering Emotional Dynamics for Art Therapy: Controllable Narrative Script Generation through Hierarchically Guided LLM Agents](https://mlsanity.com/paper/2606.16481/) (2026)

## Abstract (arXiv)

Emotion arcs capture how an individual (or a population) feels over time. They are widely used in industry and research; however, there is little work on evaluating the automatically generated arcs. This is because of the difficulty of establishing the true (gold) emotion arc. Our work, for the first time, systematically and quantitatively evaluates automatically generated emotion arcs. We also compare two common ways of generating emotion arcs: Machine-Learning (ML) models and Lexicon-Only (LexO) methods. By running experiments on 18 diverse datasets in 9 languages, we show that despite being markedly poor at instance level emotion classification, LexO methods are highly accurate at generating emotion arcs when aggregating information from hundreds of instances. We also show, through experiments on six indigenous African languages, as well as Arabic, and Spanish, that automatic translations of English emotion lexicons can be used to generate high-quality emotion arcs in less-resource languages. This opens up avenues for work on emotions in languages from around the world; which is crucial for commerce, public policy, and health research in service of speakers often left behind. Code and resources: https://github.com/dteodore/EmotionArcs