# DepecheMood++: a Bilingual Emotion Lexicon Built Through Simple Yet Powerful Techniques

arXiv: https://arxiv.org/abs/1810.03660 · HTML: https://mlsanity.com/paper/1810.03660/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Oct 2018.

## Built on by (most cited first, 11 of 11)

- [Pushing on Personality Detection from Verbal Behavior: A Transformer Meets Text Contours of Psycholinguistic Features](https://mlsanity.com/paper/2204.04629/) (2022)
- [LibertyMFD: A Lexicon to Assess the Moral Foundation of Liberty](https://mlsanity.com/paper/2209.06750/) (2022)
- [EmoDNN: Understanding emotions from short texts through a deep neural network ensemble](https://mlsanity.com/paper/2106.01706/) (2021)
- [Improving the Generalizability of Text-Based Emotion Detection by Leveraging Transformers with Psycholinguistic Features](https://mlsanity.com/paper/2212.09465/) (2022)
- [MASIVE: Open-Ended Affective State Identification in English and Spanish](https://mlsanity.com/paper/2407.12196/) (2024)
- [Guilt by Association: Emotion Intensities in Lexical Representations](https://mlsanity.com/paper/2104.08679/) (2021)
- [Exploring Hybrid and Ensemble Models for Multiclass Prediction of Mental Health Status on Social Media](https://mlsanity.com/paper/2212.09839/) (2022)
- [REDAffectiveLM: Leveraging Affect Enriched Embedding and Transformer-based Neural Language Model for Readers' Emotion Detection](https://mlsanity.com/paper/2301.08995/) (2023)
- [A Cost-aware Study of Depression Language on Social Media using Topic and Affect Contextualization](https://mlsanity.com/paper/2306.17564/) (2023)
- [Emotion Embeddings unicodex2014 Learning Stable and Homogeneous Abstractions from Heterogeneous Affective Datasets](https://mlsanity.com/paper/2308.07871/) (2023)
- [LML: A Novel Lexicon for the Moral Foundation of Liberty](https://mlsanity.com/paper/2407.11862/) (2024)

## Abstract (arXiv)

Several lexica for sentiment analysis have been developed and made available in the NLP community. While most of these come with word polarity annotations (e.g. positive/negative), attempts at building lexica for finer-grained emotion analysis (e.g. happiness, sadness) have recently attracted significant attention. Such lexica are often exploited as a building block in the process of developing learning models for which emotion recognition is needed, and/or used as baselines to which compare the performance of the models. In this work, we contribute two new resources to the community: a) an extension of an existing and widely used emotion lexicon for English; and b) a novel version of the lexicon targeting Italian. Furthermore, we show how simple techniques can be used, both in supervised and unsupervised experimental settings, to boost performances on datasets and tasks of varying degree of domain-specificity.