# How emoji and word embedding helps to unveil emotional transitions during online messaging

arXiv: https://arxiv.org/abs/2104.11032 · HTML: https://mlsanity.com/paper/2104.11032/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 46 references that are in the map.
- First posted on arXiv 23 Mar 2021.

## Builds on (most cited first, 4 of 4)

- [Exploiting Similarities among Languages for Machine Translation](https://mlsanity.com/paper/1309.4168/) (2013)
- [The Geometry of Culture: Analyzing Meaning through Word Embeddings](https://mlsanity.com/paper/1803.09288/) (2018)
- [emoji2vec: Learning Emoji Representations from their Description](https://mlsanity.com/paper/1609.08359/) (2016)
- [Semi-Supervised Affective Meaning Lexicon Expansion Using Semantic and Distributed Word Representations](https://mlsanity.com/paper/1703.09825/) (2017)

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

- [Learning affective meanings that derives the social behavior using Bidirectional Encoder Representations from Transformers](https://mlsanity.com/paper/2202.00065/) (2022)
- [emojiSpace: Spatial Representation of Emojis](https://mlsanity.com/paper/2209.09871/) (2022)

## Abstract (arXiv)

During online chats, body-language and vocal characteristics are not part of the communication mechanism making it challenging to facilitate an accurate interpretation of feelings, emotions, and attitudes. The use of emojis to express emotional feeling is an alternative approach in these types of communication. In this project, we focus on modeling a customer's emotion in an online messaging session with a chatbot. We use Affect Control Theory (ACT) to predict emotional change during the interaction. To let the customer use emojis, we also extend the affective dictionaries used by ACT. For this purpose, we mapped Emoji2vec embedding to the affective space. Our framework can find emotional change during messaging and how a customer's reaction is changed accordingly.