English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Emotion Classification based on text is a task with many applications whichhas received growing interest in recent years. This paper presents apreliminary study with the goal to help researchers and practitioners gaininsight into relatively new datasets as well as emotion classification ingeneral. We focus on three datasets that were recently presented in the relatedliterature, and we explore the performance of traditional as well asstate-of-the-art deep learning models in the presence of differentcharacteristics in the data. We also explore the use of data augmentation inorder to improve performance. Our experimental work shows that state-of-the-artmodels such as RoBERTa perform the best for all cases. We also provideobservations and discussion that highlight the complexity of emotionclassification in these datasets and test out the applicability of the modelsto actual social media posts we collected and labeled.

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