Twitter Sentiment Analysis Using K-means and Hierarchical Clustering on COVID Pandemic

Kaushik, Nainika; Bhatia, Manjot Kaur

doi:10.1007/978-981-16-2594-7_61

Cited by 9 publications

(4 citation statements)

References 11 publications

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“…They found that people’s discussions were primarily focused on malaria, influenza, and tuberculosis. Kaushik and Bhatia [ 25 ] proposed a framework for extracting tweets related to coronavirus all over the world and employed unsupervised learning techniques (K-means and hierarchical clustering algorithms) to gain insight into the situation in different countries. The study found that there were mixed emotions among people with a high degree of pessimism.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of Social Media Data on Ebola Outbreak Using Deep Learning Classifiers

Mirugwe,

Ashaba,

Namale

et al. 2024

Life

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The Ebola virus disease (EVD) is an extremely contagious and fatal illness caused by the Ebola virus. Recently, Uganda witnessed an outbreak of EVD, which generated much attention on various social media platforms. To ensure effective communication and implementation of targeted health interventions, it is crucial for stakeholders to comprehend the sentiments expressed in the posts and discussions on these online platforms. In this study, we used deep learning techniques to analyse the sentiments expressed in Ebola-related tweets during the outbreak. We explored the application of three deep learning techniques to classify the sentiments in 8395 tweets as positive, neutral, or negative. The techniques examined included a 6-layer convolutional neural network (CNN), a 6-layer long short-term memory model (LSTM), and an 8-layer Bidirectional Encoder Representations from Transformers (BERT) model. The study found that the BERT model outperformed both the CNN and LSTM-based models across all the evaluation metrics, achieving a remarkable classification accuracy of 95%. These findings confirm the reported effectiveness of Transformer-based architectures in tasks related to natural language processing, such as sentiment analysis.

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Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of Social Media Data on Ebola Outbreak Using Deep Learning Classifiers

Mirugwe,

Ashaba,

Namale

et al. 2024

Life

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show abstract

“…They found that people's discussions were primarily focused on malaria, influenza, and tuberculosis. Kaushik and Bhatia [29] proposed a framework for extracting tweets related to coronavirus all over the world and employed unsupervised learning techniques (K-means and hierarchical clustering algorithms) to gain insight into the situation in different countries. The study found that there were mixed emotions among people with a high degree of pessimism.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis of Social Media Data on Ebola Outbreak Using Deep Learning Classifiers

Mirugwe,

Ashaba,

Namale

et al. 2024

Preprint

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The Ebola virus disease (EVD) is an extremely contagious and fatal illness caused by the Ebola virus. Recently, Uganda witnessed an outbreak of EVD, which generated a lot of attention on various social media platforms. To ensure effective communication and implementation of targeted health interventions, it is crucial for stakeholders to comprehend the sentiments expressed in the posts and discussions on these online platforms. In this study, we used deep learning techniques to analyze the sentiments expressed in Ebola-related tweets during the outbreak. We explored the application of three deep learning techniques to classify the sentiments in 8,395 tweets as positive, neutral, or negative. The techniques examined included a 6-layer convolutional neural network (CNN), a 6-layer long-short-term memory model (LSTM), and an 8-layer Bidirectional Encoder Representations from Transformers (BERT) model. The study found that the BERT model outperformed both the CNN and LSTM-based models across all the evaluation metrics, achieving a remarkable classification accuracy of 95\%. These findings confirm the reported effectiveness of transformer-based architectures in tasks related to natural language processing, such as sentiment analysis.

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“…There were several works that focused on sentiment analysis of Tweets posted during the COVID-19 pandemic. Kaushik et al [ 115 ] used k-means and hierarchical clustering to perform sentiment analysis of Tweets about the COVID-19 pandemic. Jain et al [ 116 ] used deep learning to address the same research challenge.…”

Section: Literature Reviewmentioning

confidence: 99%

MonkeyPox2022Tweets: A Large-Scale Twitter Dataset on the 2022 Monkeypox Outbreak, Findings from Analysis of Tweets, and Open Research Questions

Thakur

2022

Infectious Disease Reports

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The mining of Tweets to develop datasets on recent issues, global challenges, pandemics, virus outbreaks, emerging technologies, and trending matters has been of significant interest to the scientific community in the recent past, as such datasets serve as a rich data resource for the investigation of different research questions. Furthermore, the virus outbreaks of the past, such as COVID-19, Ebola, Zika virus, and flu, just to name a few, were associated with various works related to the analysis of the multimodal components of Tweets to infer the different characteristics of conversations on Twitter related to these respective outbreaks. The ongoing outbreak of the monkeypox virus, declared a Global Public Health Emergency (GPHE) by the World Health Organization (WHO), has resulted in a surge of conversations about this outbreak on Twitter, which is resulting in the generation of tremendous amounts of Big Data. There has been no prior work in this field thus far that has focused on mining such conversations to develop a Twitter dataset. Furthermore, no prior work has focused on performing a comprehensive analysis of Tweets about this ongoing outbreak. To address these challenges, this work makes three scientific contributions to this field. First, it presents an open-access dataset of 556,427 Tweets about monkeypox that have been posted on Twitter since the first detected case of this outbreak. A comparative study is also presented that compares this dataset with 36 prior works in this field that focused on the development of Twitter datasets to further uphold the novelty, relevance, and usefulness of this dataset. Second, the paper reports the results of a comprehensive analysis of the Tweets of this dataset. This analysis presents several novel findings; for instance, out of all the 34 languages supported by Twitter, English has been the most used language to post Tweets about monkeypox, about 40,000 Tweets related to monkeypox were posted on the day WHO declared monkeypox as a GPHE, a total of 5470 distinct hashtags have been used on Twitter about this outbreak out of which #monkeypox is the most used hashtag, and Twitter for iPhone has been the leading source of Tweets about the outbreak. The sentiment analysis of the Tweets was also performed, and the results show that despite a lot of discussions, debate, opinions, information, and misinformation, on Twitter on various topics in this regard, such as monkeypox and the LGBTQI+ community, monkeypox and COVID-19, vaccines for monkeypox, etc., “neutral” sentiment was present in most of the Tweets. It was followed by “negative” and “positive” sentiments, respectively. Finally, to support research and development in this field, the paper presents a list of 50 open research questions related to the outbreak in the areas of Big Data, Data Mining, Natural Language Processing, and Machine Learning that may be investigated based on this dataset.

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Twitter Sentiment Analysis Using K-means and Hierarchical Clustering on COVID Pandemic

Cited by 9 publications

References 11 publications

Sentiment Analysis of Social Media Data on Ebola Outbreak Using Deep Learning Classifiers

Sentiment Analysis of Social Media Data on Ebola Outbreak Using Deep Learning Classifiers

Sentiment Analysis of Social Media Data on Ebola Outbreak Using Deep Learning Classifiers

MonkeyPox2022Tweets: A Large-Scale Twitter Dataset on the 2022 Monkeypox Outbreak, Findings from Analysis of Tweets, and Open Research Questions

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