A Sentiment Analysis Approach to Predict an Individual’s Awareness of the Precautionary Procedures to Prevent COVID-19 Outbreaks in Saudi Arabia

Aljameel, Sumayh S.; Alabbad, Dina A.; Alzahrani, Norah A.; Alqarni, Shouq M.; Alamoudi, Fatimah A.; Babili, Lana M.; Aljaafary, Somiah K.; Alshamrani, Fatima M.

doi:10.3390/ijerph18010218

Cited by 78 publications

(58 citation statements)

References 31 publications

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“…Barkur et al [28] explored the Twitter data for sentiments of people in India about COVID-19 lockdown, and observation showed that the majority of views about lockdown were negative but also there were some positive opinions. In another research study [29], the authors have proposed the machine learning model to predict an individual's awareness of the protective measures against the coronavirus in Saudi Arabia. In this study, Arabic tweets related to COVID-19 were collected and machine learning models: support vector machine, K-nearest neighbors, and naïve Bayes were used to train and test the Arabic tweets, SVM model outperformed with an accuracy of 85%.…”

Section: Sentiment Polarity Assessment On Covid-19 Datamentioning

confidence: 99%

Evaluating Polarity Trend Amidst the Coronavirus Crisis in Peoples’ Attitudes toward the Vaccination Drive

et al. 2021

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It has been more than a year since the coronavirus (COVID-19) engulfed the whole world, disturbing the daily routine, bringing down the economies, and killing two million people across the globe at the time of writing. The pandemic brought the world together to a joint effort to find a cure and work toward developing a vaccine. Much to the anticipation, the first batch of vaccines started rolling out by the end of 2020, and many countries began the vaccination drive early on while others still waiting in anticipation for a successful trial. Social media, meanwhile, was bombarded with all sorts of both positive and negative stories of the development and the evolving coronavirus situation. Many people were looking forward to the vaccines, while others were cautious about the side-effects and the conspiracy theories resulting in mixed emotions. This study explores users’ tweets concerning the COVID-19 vaccine and the sentiments expressed on Twitter. It tries to evaluate the polarity trend and a shift since the start of the coronavirus to the vaccination drive across six countries. The findings suggest that people of neighboring countries have shown quite a similar attitude regarding the vaccination in contrast to their different reactions to the coronavirus outbreak.

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Section: Sentiment Polarity Assessment On Covid-19 Datamentioning

confidence: 99%

Evaluating Polarity Trend Amidst the Coronavirus Crisis in Peoples’ Attitudes toward the Vaccination Drive

et al. 2021

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“…Few approaches have been proposed for sentiment analysis of Arabic COVID-19 tweets [9,42,43]. Two of these approaches employed classical machine learning algorithms to classify the tweets into positive, negative, and neutral sentiments [42,43].…”

Section: Literature Reviewmentioning

confidence: 99%

Fine-Grained Sentiment Analysis of Arabic COVID-19 Tweets Using BERT-Based Transformers and Dynamically Weighted Loss Function

Alturayeif

Luqman

2021

Applied Sciences

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The outbreak of coronavirus disease (COVID-19) has affected almost all of the countries of the world, and has had significant social and psychological effects on the population. Nowadays, social media platforms are being used for emotional self-expression towards current events, including the COVID-19 pandemic. The study of people’s emotions in social media is vital to understand the effect of this pandemic on mental health, in order to protect societies. This work aims to investigate to what extent deep learning models can assist in understanding society’s attitude in social media toward COVID-19 pandemic. We employ two transformer-based models for fine-grained sentiment detection of Arabic tweets, considering that more than one emotion can co-exist in the same tweet. We also show how the textual representation of emojis can boost the performance of sentiment analysis. In addition, we propose a dynamically weighted loss function (DWLF) to handle the issue of imbalanced datasets. The proposed approach has been evaluated on two datasets and the attained results demonstrate that the proposed BERT-based models with emojis replacement and DWLF technique can improve the sentiment detection of multi-dialect Arabic tweets with an F1-Micro score of 0.72.

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“…The recent COVID-19 pandemic strongly motivated researchers to apply Twitter sentiment analysis to related public health areas, such as the awareness of precautionary procedures for COVID-19 [ 41 ], social life impact of COVID-19 [ 42 ], concerns regarding COVID-19 [ 43 ], and emotional reactions towards COVID-19 [ 44 , 45 , 46 ]. To conduct the sentiment analysis, these studies either used pre-trained sentiment analysis models [ 45 , 46 ] or manually built annotated corpora for training the sentiment analysis models [ 41 , 42 , 43 , 44 ]. Both approaches have limitations.…”

Section: Introductionmentioning

confidence: 99%

Analyzing Twitter Data to Evaluate People’s Attitudes towards Public Health Policies and Events in the Era of COVID-19

Tsai

Wang

2021

IJERPH

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Policymakers and relevant public health authorities can analyze people’s attitudes towards public health policies and events using sentiment analysis. Sentiment analysis focuses on classifying and analyzing text sentiments. A Twitter sentiment analysis has the potential to monitor people’s attitudes towards public health policies and events. Here, we explore the feasibility of using Twitter data to build a surveillance system for monitoring people’s attitudes towards public health policies and events since the beginning of the COVID-19 pandemic. In this study, we conducted a sentiment analysis of Twitter data. We analyzed the relationship between the sentiment changes in COVID-19-related tweets and public health policies and events. Furthermore, to improve the performance of the early trained model, we developed a data preprocessing approach by using the pre-trained model and early Twitter data, which were available at the beginning of the pandemic. Our study identified a strong correlation between the sentiment changes in COVID-19-related Twitter data and public health policies and events. Additionally, the experimental results suggested that the data preprocessing approach improved the performance of the early trained model. This study verified the feasibility of developing a fast and low-human-effort surveillance system for monitoring people’s attitudes towards public health policies and events during a pandemic by analyzing Twitter data. Based on the pre-trained model and early Twitter data, we can quickly build a model for the surveillance system.

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A Sentiment Analysis Approach to Predict an Individual’s Awareness of the Precautionary Procedures to Prevent COVID-19 Outbreaks in Saudi Arabia

Cited by 78 publications

References 31 publications

Evaluating Polarity Trend Amidst the Coronavirus Crisis in Peoples’ Attitudes toward the Vaccination Drive

Evaluating Polarity Trend Amidst the Coronavirus Crisis in Peoples’ Attitudes toward the Vaccination Drive

Fine-Grained Sentiment Analysis of Arabic COVID-19 Tweets Using BERT-Based Transformers and Dynamically Weighted Loss Function

Analyzing Twitter Data to Evaluate People’s Attitudes towards Public Health Policies and Events in the Era of COVID-19

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