# EmoDNN: Understanding emotions from short texts through a deep neural network ensemble

arXiv: https://arxiv.org/abs/2106.01706 · HTML: https://mlsanity.com/paper/2106.01706/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 69 references that are in the map.
- First posted on arXiv 3 Jun 2021.

## 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)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Convolutional Neural Networks at Constrained Time Cost](https://mlsanity.com/paper/1412.1710/) (2014)
- [CNN: Single-label to Multi-label](https://mlsanity.com/paper/1406.5726/) (2014)
- [Word Affect Intensities](https://mlsanity.com/paper/1704.08798/) (2017)
- [SpanEmo: Casting Multi-label Emotion Classification as Span-prediction](https://mlsanity.com/paper/2101.10038/) (2021)
- [Emotion Intensities in Tweets](https://mlsanity.com/paper/1708.03696/) (2017)
- [DepecheMood++: a Bilingual Emotion Lexicon Built Through Simple Yet Powerful Techniques](https://mlsanity.com/paper/1810.03660/) (2018)
- [SoulMate: Short-text author linking through Multi-aspect temporal-textual embedding](https://mlsanity.com/paper/1910.12180/) (2019)
- [TEAGS: Time-aware Text Embedding Approach to Generate Subgraphs](https://mlsanity.com/paper/1907.03191/) (2019)
- [Emotion Correlation Mining Through Deep Learning Models on Natural Language Text](https://mlsanity.com/paper/2007.14071/) (2020)

## Built on by (most cited first, 2 of 2)

- [Evaluating Large Language Models for Anxiety and Depression Classification using Counseling and Psychotherapy Transcripts](https://mlsanity.com/paper/2407.13228/) (2024)
- [Modeling Induced Pleasure through Cognitive Appraisal Prediction via Multimodal Fusion](https://mlsanity.com/paper/2604.23753/) (2026)

## Abstract (arXiv)

The latent knowledge in the emotions and the opinions of the individuals that are manifested via social networks are crucial to numerous applications including social management, dynamical processes, and public security. Affective computing, as an interdisciplinary research field, linking artificial intelligence to cognitive inference, is capable to exploit emotion-oriented knowledge from brief contents. The textual contents convey hidden information such as personality and cognition about corresponding authors that can determine both correlations and variations between users. Emotion recognition from brief contents should embrace the contrast between authors where the differences in personality and cognition can be traced within emotional expressions. To tackle this challenge, we devise a framework that, on the one hand, infers latent individual aspects, from brief contents and, on the other hand, presents a novel ensemble classifier equipped with dynamic dropout convnets to extract emotions from textual context. To categorize short text contents, our proposed method conjointly leverages cognitive factors and exploits hidden information. We utilize the outcome vectors in a novel embedding model to foster emotion-pertinent features that are collectively assembled by lexicon inductions. Experimental results show that compared to other competitors, our proposed model can achieve a higher performance in recognizing emotion from noisy contents.