Automatically Classifying Emotions based on Text: A Comparative Exploration of Different Datasets

Emotion Classification based on text is a task with many applications which has received growing interest in recent years. This paper presents a preliminary study with the goal to help researchers and practitioners gain insight into relatively new datasets as well as emotion classification in genera...

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Bibliographic Details
Published in2022 IEEE 34th International Conference on Tools with Artificial Intelligence (ICTAI) pp. 342 - 346
Main Authors Koufakou, Anna, Garciga, Jairo, Paul, Adam, Morelli, Joseph, Frank, Christopher
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.10.2022
Subjects
Complexity theory
Data models
Deep learning
Emotion Classification
Emotion Detection
Emotion recognition
Natural Language Processing
Predictive models
Social networking (online)
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Summary:Emotion Classification based on text is a task with many applications which has received growing interest in recent years. This paper presents a preliminary study with the goal to help researchers and practitioners gain insight into relatively new datasets as well as emotion classification in general. We focus on three datasets that were recently presented in the related literature, and we explore the performance of traditional as well as state-of-the-art deep learning models in the presence of different characteristics in the data. We also explore the use of data augmentation in order to improve performance. Our experi-mental work shows that state-of-the-art models such as RoBERTa perform the best for all cases. We also provide observations and discussion that highlight the complexity of emotion classification in these datasets and test out the applicability of the models to actual social media posts we collected and labeled.
ISSN:2375-0197
DOI:10.1109/ICTAI56018.2022.00056