Multilabel Text Emotion Classification

Abstract: The multi-label emotion classification task aims to identify all possible emotions in a written text that best represent the author’s mental state. In recent years, multi-label emotion classification attracted the attention of researchers due to its potential applications in e-learning, health care, marketing, etc. There is a need for standard benchmark corpora to develop and evaluate multi-label emotion classification methods. The majority of benchmark corpora were developed for the English language (monolingual corpora) using tweets. The proposed work focused on English language. A multilabel emotion datasets are collected from the go emotions library. To build this project we have used both machine learning and deep learning techniques to predict the result, but compared to machine learning algorithms deep learning MLP has provided better result accuracy.

Keywords: Emotion, MLP, Multilabel, Corpora, Classification