Emotion Detection from Text via Ensemble Classification Using Word Embeddings

Emotion detection from text has become a popular task due to the key role of emotions in human-machine interaction. Current approaches represent text as a sparse bag-of-words vector. In this work, we propose a new approach that utilizes pre-trained, dense word embedding representations. We introduce an ensemble approach combining both sparse and dense representations. Our experiments include five datasets for emotion detection from different domains and show an average improvement of 11.6% in macro average F1-score.