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

Journal of computer sciences

article

Ahsan Habib¹  M. A. H. Akhand¹  Abdus Samad Kamal² 

[1] Department of Computer Science & Engineering, Khulna University of Engineering & Technology;Graduate School of Science and Technology, Gunma University

关键词: Deep Learning;    CNN;    Emotion Recognition;    Emoticons;   

DOI  :  10.3844/jcssp.2022.1170.1178

学科分类：计算机科学（综合）

来源: Science Publications

【 摘 要 】

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.

【 授权许可】

CC BY