Social media sentiment analysis to monitor the performance of vaccination coverage during the early phase of the national COVID-19 vaccine rollout

Rahmanti, Annisa Ristya; Chien, Chia‐Hui; Nursetyo, Aldilas Achmad; Husnayain, Atina; Wiratama, Bayu Satria; Fuad, Anis; Yang, Hsuan-Chia; Li, Yu-Chuan

doi:10.1016/j.cmpb.2022.106838

Cited by 15 publications

(11 citation statements)

References 24 publications

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“…Furthermore, Twitter is an ideal microblogging social media site for analysis of naturally occurring data to examine how suicide is talked about during COVID-19 pandemic, as users can upload messages of up to 280 characters of text, also referred to as “tweets”. Previous studies have found natural language processing techniques and such social media analyses to be a feasible and novel method to study public sentiment and emotional manifestations occurred on a given topic [ 15 , 16 , 17 , 18 ].…”

Section: Methodsmentioning

confidence: 99%

“…Next, during the pre-processing phase, sentences from each tweet were tokenized (i.e., reduced to single words), and cleaned (i.e., punctuation and extra spaces were removed, and words were converted into lowercase [ 14 , 17 ]. English words with high frequencies of occurrence that add little to the meaning of sentences, also known as stop words, were removed (e.g., “and”, “the”, “a”, “of”).…”

Section: Methodsmentioning

confidence: 99%

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Public Discourse Surrounding Suicide during the COVID-19 Pandemic: An Unsupervised Machine Learning Analysis of Twitter Posts over a One-Year Period

Lim

Xin

et al. 2022

IJERPH

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Many studies have forewarned the profound emotional and psychosocial impact of the protracted COVID-19 pandemic. This study thus aimed to examine how individuals relate to suicide amid the COVID-19 pandemic from a global perspective via the public Twitter discourse around suicide and COVID-19. Original Twitter tweets from 1 February 2020 to 10 February 2021 were searched, with terms related to “COVID-19”, “suicide”, or “self-harm”. An unsupervised machine learning approach and topic modelling were used to identify topics from unique tweets, with each topic further grouped into themes using manually conducted thematic analysis by the study investigators. A total of 35,904 tweets related to suicide and COVID-19 were processed into 42 topics and six themes. The main themes were: (1) mixed reactions to COVID-19 public health policies and their presumed impact on suicide; (2) biopsychosocial impact of COVID-19 pandemic on suicide and self-harm; (3) comparing mortality rates of COVID-19, suicide, and other leading causes of death; (4) mental health support for individuals at risk of suicide; (5) reported cases and public reactions to news related to COVID-19, suicide, and homicide; and (6) figurative usage of the word suicide. The general public was generally concerned about governments’ responses as well as the perturbing effects on mental health, suicide, the economy, and at-risk populations.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Public Discourse Surrounding Suicide during the COVID-19 Pandemic: An Unsupervised Machine Learning Analysis of Twitter Posts over a One-Year Period

Lim

Xin

et al. 2022

IJERPH

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“…MNB Model (Multinomial Naive Bayes) [ 25 ]. It is a representative of traditional machine learning and has achieved excellent results in a large number of multiclassification tasks.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…e same parameters are used in the two experiments in this study, and the specific experimental parameter settings are listed in Table 2. [25]. It is a representative of traditional machine learning and has achieved excellent results in a large number of multiclassification tasks.…”

Section: Experimental Parameter Settingsmentioning

confidence: 99%

Integrating Multiclass Light Weighted BiLSTM Model for Classifying Negative Emotions

Bhende¹,

Thakare²,

Pant

et al. 2022

Computational Intelligence and Neuroscience

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With the continuous development of social networks, Weibo has become an essential platform for people to share their opinions and feelings in daily life. Analysis of users’ emotional tendencies can be effectively applied to public opinion control, public opinion surveys, and product recommendations. However, the traditional deep learning algorithm often needs a large amount of data to be retained to obtain a better accuracy when faced with new work tasks. Given this situation, a multiclassification method of microblog negative sentiment based on MAML (model-agnostic metalearning) and BiLSTM (bidirectional extended short-term memory network) is proposed to represent the microblog text word vectorization and the combination of MAML and BiLSTM is constructed. The model of BiLSTM realizes the classification of negative emotions on Weibo and updates the parameters through machine gradient descent; the metalearner in MAML calculates the sum of the losses of multiple pieces of training, performs a second gradient descent, and updates the metalearner parameters. The updated metalearner can quickly iterate when faced with a new Weibo negative sentiment classification task. The experimental results show that compared with the prepopular model, on the Weibo negative sentiment dataset, the precision rate, recall rate, and F1 value are increased by 1.68 percentage points, 2.86 percentage points, and 2.27 percentage points, respectively.

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“…Therefore, it is essential to keep an eye on the dissemination of false information about the COVID-19 vaccine and to lessen its effects in order to boost public trust and effectively combat vaccination reluctance. A large-scale real-time tracking tool for disease outbreaks and government epidemic control measures was routinely used on social media throughout the worldwide pandemic [6].…”

Section: Introductionmentioning

confidence: 99%

Opinions for Receiving COVID-19 Vaccines Based on Sentiment Analysis

2022

pnr

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Due to vaccination hesitancy/refusal, a call to action is urgently needed for the COVID-19 vaccine deployment. Find out what the public thinks about the COVID-19 vaccine. Sentiment analysis is a technique for getting text sentiment scores. Therefore, we proposed architecture to analyze the textual data collection of people's opinions on COVID-19 vaccines using two of the best sentiment analysis techniques, the Bidirectional Encoder Representations from Transformers (BERT) technique and the Valence Aware Dictionary for sEntiment Reasoning (VADER) technique of Natural Language Processing (NLP). A questionnaire survey of corona vaccines recipients who recommend COVID-19 collected the data. Finally, recommendations for the corona vaccine were investigated, and various studies were done to determine its efficacy. Accuracy, precision, recall, and f1-score are standard evaluation criteria. The data shows the proposed model's excellent sentiment analysis performance, indicating that most vaccine users prefer to recommend others to get the vaccines.

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Social media sentiment analysis to monitor the performance of vaccination coverage during the early phase of the national COVID-19 vaccine rollout

Cited by 15 publications

References 24 publications

Public Discourse Surrounding Suicide during the COVID-19 Pandemic: An Unsupervised Machine Learning Analysis of Twitter Posts over a One-Year Period

Public Discourse Surrounding Suicide during the COVID-19 Pandemic: An Unsupervised Machine Learning Analysis of Twitter Posts over a One-Year Period

Integrating Multiclass Light Weighted BiLSTM Model for Classifying Negative Emotions

Opinions for Receiving COVID-19 Vaccines Based on Sentiment Analysis

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