Research Article Open Access

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

Md. Ahsan Habib1, M. A. H. Akhand1 and Md. Abdus Samad Kamal2
  • 1 Department of Computer Science & Engineering, Khulna University of Engineering & Technology, Bangladesh
  • 2 Graduate School of Science and Technology, Gunma University, Japan

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.

Journal of Computer Science
Volume 18 No. 12, 2022, 1170-1178

DOI: https://doi.org/10.3844/jcssp.2022.1170.1178

Submitted On: 17 October 2022 Published On: 7 December 2022

How to Cite: Habib, M. A., Akhand, M. A. H. & Kamal, M. A. S. (2022). Emotion Recognition from Microblog Managing Emoticon with Text and Classifying using 1D CNN. Journal of Computer Science, 18(12), 1170-1178. https://doi.org/10.3844/jcssp.2022.1170.1178

  • 1,941 Views
  • 727 Downloads
  • 3 Citations

Download

Keywords

  • Deep Learning
  • CNN
  • Emotion Recognition
  • Emoticons