Word mover's distance for affect detection
Mohammed Alshahrani, Spyridon Samothrakis, Maria Fasli · 2017
Emotion detection from text has been an important task in Natural language processing (NLP) for many years. Many approaches were based on the emotional words or lexicon in order to detect emotions. While the word embedding vectors like Word2Vec were successfully employed in many NLP approaches. Also, word mover's distance (WMD) is a method introduced recently to calculate the distance between two documents based on the embedded words. However, this paper is investigating the ability to detect or classify emotions in sentences using word vectorization and distance measures. Our results confirm the novelty of using Word2Vec and WMD in predicting the emotions in text.