Intelligent lead-based bidirectional long short term memory for COVID-19 sentiment analysis

Abstract

Social media is an online platform with millions of users and is utilized to spread news, information, world events, discuss ideas, etc. During the COVID-19 pandemic, information and ideas are shared by users both officially and by citizens. Here, the detection of useful content from social media is a challenging task. Hence, natural language processing (NLP) and deep learning are widely utilized for the analysis of the emotions of people during the COVID-19 pandemic. Hence, this research introduces a deep learning mechanism for identifying the sentiment of the people by considering the online Twitter data regarding COVID-19. The intelligent lead-based BiLSTM is utilized to analyze people's sentiments. Here, the loss of the classifier while learning the data is eliminated through the incorporation of the intelligent lead optimization. Hence, the loss is reduced, and a more accurate analysis is obtained. The intelligent lead optimization is devised by considering the role of the informer in identifying the enemy base to safeguard the territory from attack along with the Monarch's knowledge. The performance of the intelligent lead-based BiLSTM for the sentiment analysis is assessed using the metrics like accuracy, sensitivity, and specificity and obtained the values of 96.11, 99.22, and 95.35%, respectively, which are 14.24, 10.45, and 26.57% enhanced performance compared to the baseline KNN technique.

Polarity-Based Sentiment Analysis of Georeferenced Tweets Related to the 2022 Twitter Acquisition

“…Various deep learning methods have also been applied for sentiment analyses. For instance, [25] use a bidirectional long shortterm memory (BiLSTM) to classify sentiments regarding COVID-19, obtaining very high accuracy and sensitivity. The COVID-19 pandemic in general has been a popular research object for social media-based sentiment analysis.…”

We chased COVID-19; Did we forget Measles? - A machine learning approach to public discourse and sentiment analysis on spiking Measles cases

“…Social media is proven to be a platform for deliberations and discussions on any topics, including pandemics and disease outbreaks [3][4][5] [6]. The public commonly shares their opinions, concerns, symptoms, treatments, and adverse reactions to medications on social media such as Twitter [7][8] and Facebook and news aggregators such as Reddit [9] [10]. These highly invaluable data contain trends, and latent themes that may be significantly important to aid public policymakers and governments in prioritizing their strategies to help combat the outbreaks [11][12] [13].…”