Title: Integration of deep learning techniques for sentiment and emotion analysis of social media data

Authors: H.S. Hota; Dinesh K. Sharma; Nilesh Verma

Addresses: Department of Computer Science and Application, Atal Bihari Vajpayee University, India ' Department of Business, Management and Accounting, University of Maryland Eastern Shore, Princess Anne, MD 21853, USA ' Department of Computer Science and Application, Atal Bihari Vajpayee University, India

Abstract: Sentiment analysis (SA) and emotion analysis (EA) are commonly used to understand people's feelings and opinions on a given topic. COVID-19 is an emerging infectious disease that is rapidly spreading around the world. The mental state of a country's population is more or less the same worldwide. Machine learning (ML) techniques are commonly utilised to analyse human sentiments and emotions. Two popular deep learning (DL) techniques: convolutional neural network (CNN) and long short-term memory (LSTM) are being applied in several areas. In this study, we propose a hybrid of CNN and LSTM to improve the performance of the classification model. The two different models, the sentiment analysis model (SAM) and the emotional analysis model (EAM), were developed using benchmark data, which produces 91.11% and 89.39% accuracy, respectively, by integrating CNN and LSTM. Integration of two or more techniques significantly improves performance by utilising both techniques. The results of the experiments demonstrate that the proposed hybrid technique outperforms other individual DL techniques.

Keywords: convolutional neural network; CNN; sentiment analysis; SA; emotion analysis; EA; COVID-19; deep learning; DL; long short-term memory; LSTM.

DOI: 10.1504/IJISTA.2023.130552

International Journal of Intelligent Systems Technologies and Applications, 2023 Vol.21 No.1, pp.1 - 20

Received: 15 Jun 2021
Received in revised form: 02 Sep 2022
Accepted: 08 Sep 2022
Published online: 27 Apr 2023 *