Contextual Emotion Detection in Text Using Ensemble Learning

Abstract

As human beings, it is hard to interpret the presence of emotions such as sadness or disgust in a sentence without the context, and the same ambiguity exists for machines also. Emotion detection from facial expressions and voice modulation easier than emotion detection from text. Contextual emotion detection from text is a challenging problem in text mining. Contextual emotion detection is gaining importance, as people these days are communicating mainly through text messages, to provide emotionally aware responses to the users. This work demonstrates ensemble learning to detect emotions present in a sentence. Ensemble models like Random Forest, Adaboost and Gradient Boosting have been used to detect emotions. Out of the three models, it has been found that Gradient Boosting Classifiers predicts the emotions better than the other two classifiers.