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 covered single-label classification and neglected the presence of multiple emotion
labels in one instance. This paper presents multi-emotion classification in Twitter based on Convolutional
Neural Networks (CNNs). The applied features are emotion lexicons, word embeddings and frequency
distribution. The proposed networks performance is evaluated using state-of-the-art classification algorithms,
achieving a hamming score range from 0.46 to 0.52 on the challenging SemEval2018 Task E-c.
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