Joint Emotion Label Space Modelling for Affect Lexica

by   Luna De Bruyne, et al.

Emotion lexica are commonly used resources to combat data poverty in automatic emotion detection. However, methodological issues emerge when employing them: lexica are often not very extensive, and the way they are constructed can vary widely – from lab conditions to crowdsourced approaches and distant supervision. Furthermore, both categorical frameworks and dimensional frameworks coexist, in which theorists provide many different sets of categorical labels or dimensional axes. The heterogenous nature of the resulting emotion detection resources results in a need for a unified approach to utilising them. This paper contributes to the field of emotion analysis in NLP by a) presenting the first study to unify existing emotion detection resources automatically and thus learn more about the relationships between them; b) exploring the use of existing lexica for the above-mentioned task; c) presenting an approach to automatically combining emotion lexica, namely by a multi-view variational auto-encoder (VAE), which facilitates the mapping of datasets into a joint emotion label space. We test the utility of joint emotion lexica by using them as additional features in state-of-the art emotion detection models. Our overall findings are that emotion lexica can offer complementary information to even extremely large pre-trained models such as BERT. The performance of our models is comparable to state-of-the art models that are specifically engineered for certain datasets, and even outperform the state-of-the art on four datasets.


Morphset:Augmenting categorical emotion datasets with dimensional affect labels using face morphing

Emotion recognition and understanding is a vital componentin human-machi...

Toward Dimensional Emotion Detection from Categorical Emotion Annotations

We propose a framework which makes a model predict fine-grained dimensio...

Unifying the Discrete and Continuous Emotion labels for Speech Emotion Recognition

Traditionally, in paralinguistic analysis for emotion detection from spe...

Cluster-Level Contrastive Learning for Emotion Recognition in Conversations

A key challenge for Emotion Recognition in Conversations (ERC) is to dis...

Distant supervision for emotion detection using Facebook reactions

We exploit the Facebook reaction feature in a distant supervised fashion...

Towards a Unified Framework for Emotion Analysis

We present EmoCoder, a modular encoder-decoder architecture that general...

Multi-view Multi-label Fine-grained Emotion Decoding from Human Brain Activity

Decoding emotional states from human brain activity plays an important r...

Please sign up or login with your details

Forgot password? Click here to reset