Granular emotion detection for multi-class sentiment analysis in social media

To address the challenges of granular emotion detection in social media text (EMDISM), I have investigated ensemble approaches that combine a variety of individual classifiers to address tradeoffs in performance. This involved first investigating EMDISM performance for individual traditional machine learning (ML), deep learning (DL), and transformer learning (TL) classifiers. Based on this analysis, the second stage investigated the creation of ensembles of the most accurate classifiers across these general classes which offer comparatively improved performance. I provide results and analysis for each classifier I considered as well as the most accurate ensembles I created from the most accurate singleton classifiers. Results show that the proposed ensemble approaches improve upon the state of the art for average accuracy, weighted precision, weighted recall, and weighted f-measure as compared to the most accurate single classifier for EMDISM.