# Gated Recurrent Neural Network Approach for Multilabel Emotion Detection in Microblogs

arXiv: https://arxiv.org/abs/1907.07653 · HTML: https://mlsanity.com/paper/1907.07653/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2019.

## Built on by (most cited first, 4 of 4)

- [A Survey of Methods for Addressing Class Imbalance in Deep-Learning Based Natural Language Processing](https://mlsanity.com/paper/2210.04675/) (2022)
- [A Review on Text-Based Emotion Detection -- Techniques, Applications, Datasets, and Future Directions](https://mlsanity.com/paper/2205.03235/) (2022)
- [GuReT: Distinguishing Guilt and Regret related Text](https://mlsanity.com/paper/2401.16541/) (2024)
- [How to Estimate Whether You Have Found Several Needles in a Haystack: Measuring Calibration in Multi-Label Text Classification](https://mlsanity.com/paper/2609.26468/) (2026)

## Abstract (arXiv)

People express their opinions and emotions freely in social media posts and online reviews that contain valuable feedback for multiple stakeholders such as businesses and political campaigns. Manually extracting opinions and emotions from large volumes of such posts is an impossible task. Therefore, automated processing of these posts to extract opinions and emotions is an important research problem. However, human emotion detection is a challenging task due to the complexity and nuanced nature. To overcome these barriers, researchers have extensively used techniques such as deep learning, distant supervision, and transfer learning. In this paper, we propose a novel Pyramid Attention Network (PAN) based model for emotion detection in microblogs. The main advantage of our approach is that PAN has the capability to evaluate sentences in different perspectives to capture multiple emotions existing in a single text. The proposed model was evaluated on a recently released dataset and the results achieved the state-of-the-art accuracy of 58.9%.