A Hybrid AI Model for Improving COVID-19 Sentiment Analysis in Social Networks
2022 IEEE International Conference on Communications, ICC 2022
; 2022-May:1752-1757, 2022.
Article
in English
| Scopus | ID: covidwho-2029236
ABSTRACT
The recent COVID-19 (novel coronavirus disease) pandemic induced a deep polarization among regional as well as global communities. The sentiments regarding the pandemic and its impact on lifestyle and economy, often expressed via social networks, are regarded as critical metrics for capturing such polarization and formulating appropriate intervention by the relevant authorities. While there exist a myriad of Natural Language Processing (NLP) models for mining social media data, we demonstrate the shortcomings of the individual models in this paper, and explore how to improve the COVID-19 sentiment analysis in social media network data via two hybrid predictive models based on a Long-Short-Term-Memory (LSTM)-based autoencoder and a Convolutional Neural Network (CNN) model coupled with a bi-directional LSTM. Through extensive experiments on the recently acquired Twitter dataset, we compare the COVID-19 sentiments exhibited in the USA and Canada using our proposed hybrid predictive models and demonstrate their superiority over individual Artificial Intelligence (AI) models. © 2022 IEEE.
a Long-Short-Term-Memory (LSTM); Convolutional Neural Network (CNN); COVID-19; Natural Language Processing; Sentiment Analysis; Brain; Convolution; Convolutional neural networks; Long short-term memory; Polarization; Social networking (online); A long-short-term-memory; Convolutional neural network; Hybrid artificial intelligences; Intelligence models; Language processing; Natural languages; Predictive models
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
2022 IEEE International Conference on Communications, ICC 2022
Year:
2022
Document Type:
Article
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