# Learning affective meanings that derives the social behavior using Bidirectional Encoder Representations from Transformers

arXiv: https://arxiv.org/abs/2202.00065 · HTML: https://mlsanity.com/paper/2202.00065/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 21 references that are in the map.
- First posted on arXiv 31 Jan 2022.

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

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [The Geometry of Culture: Analyzing Meaning through Word Embeddings](https://mlsanity.com/paper/1803.09288/) (2018)
- [Semi-Supervised Affective Meaning Lexicon Expansion Using Semantic and Distributed Word Representations](https://mlsanity.com/paper/1703.09825/) (2017)
- [How emoji and word embedding helps to unveil emotional transitions during online messaging](https://mlsanity.com/paper/2104.11032/) (2021)

## Abstract (arXiv)

Predicting the outcome of a process requires modeling the system dynamic and observing the states. In the context of social behaviors, sentiments characterize the states of the system. Affect Control Theory (ACT) uses sentiments to manifest potential interaction. ACT is a generative theory of culture and behavior based on a three-dimensional sentiment lexicon. Traditionally, the sentiments are quantified using survey data which is fed into a regression model to explain social behavior. The lexicons used in the survey are limited due to prohibitive cost. This paper uses a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model to develop a replacement for these surveys. This model achieves state-of-the-art accuracy in estimating affective meanings, expanding the affective lexicon, and allowing more behaviors to be explained.