# DepecheMood: a Lexicon for Emotion Analysis from Crowd-Annotated News

arXiv: https://arxiv.org/abs/1405.1605 · HTML: https://mlsanity.com/paper/1405.1605/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2014.

## Built on by (most cited first, 13 of 13)

- [NRC VAD Lexicon v2: Norms for Valence, Arousal, and Dominance for over 55k English Terms](https://mlsanity.com/paper/2503.23547/) (2025)
- [Detection of financial opportunities in micro-blogging data with a stacked classification system](https://mlsanity.com/paper/2404.07224/) (2024)
- [LibertyMFD: A Lexicon to Assess the Moral Foundation of Liberty](https://mlsanity.com/paper/2209.06750/) (2022)
- [A Robust Deep Ensemble Classifier for Figurative Language Detection](https://mlsanity.com/paper/2107.04372/) (2021)
- [Modelling Emotion Dynamics in Song Lyrics with State Space Models](https://mlsanity.com/paper/2210.09434/) (2022)
- [Dictionary-Assisted Supervised Contrastive Learning](https://mlsanity.com/paper/2210.15172/) (2022)
- [MASIVE: Open-Ended Affective State Identification in English and Spanish](https://mlsanity.com/paper/2407.12196/) (2024)
- [Breaking Bad: Norms for Valence, Arousal, and Dominance for over 10k English Multiword Expressions](https://mlsanity.com/paper/2511.19816/) (2025)
- [Guilt by Association: Emotion Intensities in Lexical Representations](https://mlsanity.com/paper/2104.08679/) (2021)
- [Tracking Turbulence Through Financial News During COVID-19](https://mlsanity.com/paper/2109.04369/) (2021)
- ["That's so cute!": The CARE Dataset for Affective Response Detection](https://mlsanity.com/paper/2201.11895/) (2022)
- [Blacks is to Anger as Whites is to Joy? Understanding Latent Affective Bias in Large Pre-trained Neural Language Models](https://mlsanity.com/paper/2301.09003/) (2023)
- [LML: A Novel Lexicon for the Moral Foundation of Liberty](https://mlsanity.com/paper/2407.11862/) (2024)

## Abstract (arXiv)

While many lexica annotated with words polarity are available for sentiment analysis, very few tackle the harder task of emotion analysis and are usually quite limited in coverage. In this paper, we present a novel approach for extracting - in a totally automated way - a high-coverage and high-precision lexicon of roughly 37 thousand terms annotated with emotion scores, called DepecheMood. Our approach exploits in an original way 'crowd-sourced' affective annotation implicitly provided by readers of news articles from rappler.com. By providing new state-of-the-art performances in unsupervised settings for regression and classification tasks, even using a na"ive approach, our experiments show the beneficial impact of harvesting social media data for affective lexicon building.