Hybrid Deep Model for Sentiment Analysis of COVID-19 Twitter Data

Abstract

Sentiment analysis is a natural language processing technique used to analyse textual data generated on social media platforms like Facebook and Twitter. Ever since the Covid19 pandemic started many posts were shared on the social media platform as videos and messages with real-time updates about the spread of the pandemic across all countries. Several misconceptions led the public to panic in addition to the health deterioration created by the disease due to the false information spread through social media. This has paved the way for this research on the sentiment analysis of reviews posted on Twitter related to the spread of Covid-19 disease. The dataset used for the proposed work is taken from the IEEE data port which is an open access dataset platform. The Hybrid Deep Sentiment Analysis (HDSA) model which is a fusion of the deep learning algorithms is employed in this work to analyse the sentiments in Covid-19 tweets. Stacked Denoising autoencoders are used for feature extraction from the dataset. Bi-Convolutional neural networks and Bi-Long Short-Term Memory Networks are used to reduce the feature dimensionality and obtain the long-term dependencies in the extracted data. The classification of the sentiments is implemented using the GANBERT technique. The proposed model exhibited 94°/0 accuracy compared to the other state-of-the-art models in the research of Sentiment Analysis of Covid-19-related tweets.