# Neural Emoji Recommendation in Dialogue Systems

arXiv: https://arxiv.org/abs/1612.04609 · HTML: https://mlsanity.com/paper/1612.04609/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2016.

## Built on by (most cited first, 3 of 3)

- [Towards Expressive Communication with Internet Memes: A New Multimodal Conversation Dataset and Benchmark](https://mlsanity.com/paper/2109.01839/) (2021)
- [Selecting Stickers in Open-Domain Dialogue through Multitask Learning](https://mlsanity.com/paper/2209.07697/) (2022)
- [Semantics Preserving Emoji Recommendation with Large Language Models](https://mlsanity.com/paper/2409.10760/) (2024)

## Abstract (arXiv)

Emoji is an essential component in dialogues which has been broadly utilized on almost all social platforms. It could express more delicate feelings beyond plain texts and thus smooth the communications between users, making dialogue systems more anthropomorphic and vivid. In this paper, we focus on automatically recommending appropriate emojis given the contextual information in multi-turn dialogue systems, where the challenges locate in understanding the whole conversations. More specifically, we propose the hierarchical long short-term memory model (H-LSTM) to construct dialogue representations, followed by a softmax classifier for emoji classification. We evaluate our models on the task of emoji classification in a real-world dataset, with some further explorations on parameter sensitivity and case study. Experimental results demonstrate that our method achieves the best performances on all evaluation metrics. It indicates that our method could well capture the contextual information and emotion flow in dialogues, which is significant for emoji recommendation.