# ANA at SemEval-2019 Task 3: Contextual Emotion detection in Conversations through hierarchical LSTMs and BERT

arXiv: https://arxiv.org/abs/1904.00132 · HTML: https://mlsanity.com/paper/1904.00132/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Mar 2019.

## Built on by (most cited first, 12 of 12)

- [Deep Emotion Recognition in Textual Conversations: A Survey](https://mlsanity.com/paper/2211.09172/) (2022)
- [ArmanEmo: A Persian Dataset for Text-based Emotion Detection](https://mlsanity.com/paper/2207.11808/) (2022)
- [AER-LLM: Ambiguity-aware Emotion Recognition Leveraging Large Language Models](https://mlsanity.com/paper/2409.18339/) (2024)
- [M2R2: Missing-Modality Robust emotion Recognition framework with iterative data augmentation](https://mlsanity.com/paper/2205.02524/) (2022)
- [Emotion Classification in a Resource Constrained Language Using Transformer-based Approach](https://mlsanity.com/paper/2104.08613/) (2021)
- [Emo Pillars: Knowledge Distillation to Support Fine-Grained Context-Aware and Context-Less Emotion Classification](https://mlsanity.com/paper/2504.16856/) (2025)
- [Simulated Annealing for Emotional Dialogue Systems](https://mlsanity.com/paper/2109.10715/) (2021)
- [Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus](https://mlsanity.com/paper/2210.06405/) (2022)
- [Dynamic Causal Disentanglement Model for Dialogue Emotion Detection](https://mlsanity.com/paper/2309.06928/) (2023)
- ["That's so cute!": The CARE Dataset for Affective Response Detection](https://mlsanity.com/paper/2201.11895/) (2022)
- [LDEB -- Label Digitization with Emotion Binarization and Machine Learning for Emotion Recognition in Conversational Dialogues](https://mlsanity.com/paper/2306.02193/) (2023)
- [Emotion Detection with Transformers: A Comparative Study](https://mlsanity.com/paper/2403.15454/) (2024)

## Abstract (arXiv)

This paper describes the system submitted by ANA Team for the SemEval-2019 Task 3: EmoContext. We propose a novel Hierarchical LSTMs for Contextual Emotion Detection (HRLCE) model. It classifies the emotion of an utterance given its conversational context. The results show that, in this task, our HRCLE outperforms the most recent state-of-the-art text classification framework: BERT. We combine the results generated by BERT and HRCLE to achieve an overall score of 0.7709 which ranked 5th on the final leader board of the competition among 165 Teams.