Emotions in texts

Abstract

Several studies have used different techniques to detect and identify emotions expressed in various sets of texts corpora. In this paper, we review different emotion models, emotion datasets and the corresponding techniques used for emotion analysis in past studies. We observe that researchers have been using a wide variety of techniques to detect emotions in texts and that there is currently no gold standard on which dataset or which emotion model to use. Consequently, although the field of emotion analysis has gained much momentum in previous years, there seems to be little progress into relevant research with findings that may be useful in real world applications. From our analysis and findings, we urge researchers to consider the development of datasets, evaluation benchmarks and a common platform for sharing achievements in emotion analysis to see further development in the field.

A Comparison of Machine Learning Models to Prioritise Emails using Emotion Analysis for Customer Service Excellence

“…Several improvements can still be made to obtain a better-performing email prioritizing solution to the email overload problem. For instance, as discussed in [12], other emotion models can be used for the data labeling step. Using lesser emotion categories could also increase accuracy, as observed by [6].…”