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A STUDY OF TRANSFORMER MODELS FOR EMOTION CLASSIFICATION IN INFORMAL TEXT

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thesis
posted on 07.01.2022, 20:54 by Alvaro S EsperancaAlvaro S Esperanca
Textual emotion classification is a task in affective AI that branches from sentiment analysis and focuses on identifying emotions expressed in a given text excerpt.
It has a wide variety of applications that improve human-computer interactions, particularly to empower computers to understand subjective human language better.
Significant research has been done on this task, but very little of that research leverages one of the most emotion-bearing symbols we have used in modern communication: Emojis.
In this thesis, we propose several transformer-based models for emotion classification that processes emojis as input tokens and leverages pretrained models and uses them
, a model that processes Emojis as textual inputs and leverages DeepMoji to generate affective feature vectors used as reference when aggregating different modalities of text encoding.
To evaluate ReferEmo, we experimented on the SemEval 2018 and GoEmotions datasets, two benchmark datasets for emotion classification, and achieved competitive performance compared to state-of-the-art models tested on these datasets. Notably, our model performs better on the underrepresented classes of each dataset.

History

Degree Type

Master of Science

Department

Electrical and Computer Engineering

Campus location

Indianapolis

Advisor/Supervisor/Committee Chair

Brian King

Advisor/Supervisor/Committee co-chair

Xiao Luo

Additional Committee Member 2

Zhenming Ding