Emotion Recognition from Microblog Managing Emoticon with Text and Classifying using 1D CNN

TL;DR

This paper presents a novel emotion recognition approach from microblogs that combines text and emoticons using a 1D CNN, achieving superior accuracy on Twitter data.

Contribution

It introduces a method that leverages both textual content and emoticons with a 1D CNN for improved emotion classification in microblogs.

Findings

Outperforms existing emotion recognition methods on Twitter data

Utilizes combined text and emoticon features for better accuracy

Demonstrates effectiveness of 1D CNN in microblog emotion classification

Abstract

Microblog, an online-based broadcast medium, is a widely used forum for people to share their thoughts and opinions. Recently, Emotion Recognition (ER) from microblogs is an inspiring research topic in diverse areas. In the machine learning domain, automatic emotion recognition from microblogs is a challenging task, especially, for better outcomes considering diverse content. Emoticon becomes very common in the text of microblogs as it reinforces the meaning of content. This study proposes an emotion recognition scheme considering both the texts and emoticons from microblog data. Emoticons are considered unique expressions of the users' emotions and can be changed by the proper emotional words. The succession of emoticons appearing in the microblog data is preserved and a 1D Convolutional Neural Network (CNN) is employed for emotion classification. The experimental result shows that the proposed emotion recognition scheme outperforms the other existing methods while tested on Twitter data.