CONVOLUTIONAL NEURAL NETWORK MULTI-EMOTION CLASSIFIERS

Abstract

The natural languages are universal and flexible but cannot exist without ambiguity. Having more than one attitude and meaning in the same phrase context is the main cause for word or phrase ambiguity. Most previous work on emotion analysis has only coverage single-label classification and neglect the presence of multiple emotion labels in one instance. This paper presents multi emotion classification in Twitter based on Convolutional Neural Networks (CNN). The applied features are emotion lexicons, word embeddings, and frequency distribution. The proposed networks performance is evaluated to state-of-the-art classification algorithms, achieving hamming score ranges from 0.46 to 0.52 on the challenging SemEval2018 Task E-c.