Leveraging Emotion-Specific features to improve Transformer performance for Emotion Classification (2022)

Abstract: This paper describes team PVG’s AI Club’s approach to the Emotion Classification shared task held at WASSA 2022. This Track 2 sub-task focuses on building models which can predict a multi-class emotion label based on essays from news articles where a person, group or another entity is affected. Baseline transformer models have been demonstrating good results on sequence classification tasks, and we aim to improve this performance with the help of ensembling techniques, and by leveraging two variations of emotion-specific representations. We observe better results than our baseline models and achieve an accuracy of 0.619 and a macro F1 score of 0.520 on the emotion classification task.

## introduction rapid growth in the availability of humanannotated text documents has led to an increase in methodologies for tasks such as classification, clustering and knowledge extraction. a multitude of sources have enabled public access to structured and semi-structured data comprising of news stories, written repositories, blog content, among countless other roots of information. @xcite showed that the task of emotion classification has emerged from being purely research oriented to being of vital importance in fields like dialog systems, intelligent agents, and analysis and diagnosis of mental disorders. humans themselves sometimes find it tough to comprehend the various layers of subtlety in emotions, and hence there has been only a limited amount of prior research revolving around emotion classification. it has been noted that larger deep learning models can also find it quite challenging to fully grasp the nuances and underlying context of human emotion. ## dataset the dataset provided by the organizers consists of 1860 essays in the training set, 270 in the dev set and 525 in the test set. each of these essays has been annotated for empathy and empathy scores, distress and distress scores, emotion, personality feature and interpersonal reactivity features. since this paper describes an approach only to the emotion classification task, we shall only describe the data for said subtask. each essay has been assigned an emotion class similar to classes in @xcite . ## results and discussion the results for the emotion prediction task on the validation set are given in ## conclusion the goal of this study was to examine and enhance the performance of transformer models using only the empathetic concern in news stories dataset that was provided to us, with the prospective of testing our method on a bigger dataset in the future. we proposed a model ensemble which combined the transformer feature vector with the emotion-intensive word embeddings along with the word-specific features obtained from the nrc lexicon. we demonstrate results that outperform the baseline vanilla roberta model, and attest that combining domain-specific features can indeed improve performance on a task as involute as emotion classification.