Multilabeled Emotions Classification in Software Engineering Text Using Convolutional Neural Networks and Word Embeddings

Abstract

Effective collaboration among software developers relies heavily on their ability to communicate efficiently, with emotions playing a pivotal role in this process. Emotions are widely used in human decision-making, making automated tools for emotion classification within developer communication channels essential. These tools can enhance productivity and collaboration by increasing awareness of fellow developers' emotions. Previous approaches, such as HOMER, RAKEL, and EmoTxt, have been proposed to classify emotions in Stack Overflow and Jira datasets at a finer granularity. However, these tools face performance challenges. To address these limitations, we aim to enhance multilabeled emotion classification performance by leveraging TextCNN, word embeddings, and hyper-parameter optimization. We validate the performance of this method by comparing it with the best previous methods for emotion classification in software engineering text. This approach achieves an F1-Micro score of 84.6001% on the Jira dataset and 76.9366% on the Stack Overflow dataset, showing an improvement of 3.5001% and 8.6366%, respectively. This advancement underscores the potential of this method in improving emotion classification performance, thereby fostering better collaboration and productivity among software developers.