A Deep Learning-Based Approach for Multi-Label Emotion Classification in Tweets

Mohammed Jabreel,Antonio Moreno

doi:10.3390/app9061123

Mohammed Jabreel, Antonio Moreno

Open Access

https://doi.org/10.3390/app9061123

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Journal: Applied sciences	Publication Date: Mar 17, 2019
Citations: 102	License type: CC BY 4.0

Affiliation: Rovira i Virgili University, Hodeidah University

Abstract

Currently, people use online social media such as Twitter or Facebook to share their emotions and thoughts. Detecting and analyzing the emotions expressed in social media content benefits many applications in commerce, public health, social welfare, etc. Most previous work on sentiment and emotion analysis has only focused on single-label classification and ignored the co-existence of multiple emotion labels in one instance. This paper describes the development of a novel deep learning-based system that addresses the multiple emotion classification problem in Twitter. We propose a novel method to transform it to a binary classification problem and exploit a deep learning approach to solve the transformed problem. Our system outperforms the state-of-the-art systems, achieving an accuracy score of 0.59 on the challenging SemEval2018 Task 1:E-cmulti-label emotion classification problem.

Highlights

Emotions are the key to people’s feelings and thoughts
In this article, we focus on the multi-label emotion classification task, which aims to develop an automatic system to determine the existence in a text of none, one, or more out of eleven emotions: the eight Plutchik [9] categories that are shown in Figure 1, plus optimism, pessimism, and love
Attentive deep learning system, which we call Binary Neural Network (BNet), which works on the new transformation method

Summary

Introduction

Emotions are the key to people’s feelings and thoughts. Online social media, such as Twitter and Facebook, have changed the language of communication. Analyzing the emotions expressed in social media content has attracted researchers in the natural language processing research field. It has a wide range of applications in commerce, public health, social welfare, etc. It can be used in public health [1,2], public opinion detection about political tendencies [3,4], brand management [5], and stock market monitoring [6]. The attitude can be the polarity (positive or negative) or an emotional state such as joy, anger, or sadness [7]

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Conclusion