Sentiment analysis of Twitter posts related to the COVID-19 vaccines

Alabid, Noralhuda N.; Katheeth, Zainab Dalaf

doi:10.11591/ijeecs.v24.i3.pp1727-1734

Cited by 13 publications

(15 citation statements)

References 49 publications

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“…A machine learning algorithm (ML) is a two-step process, training, and testing dataset. Thus, we need to label some of the tweets with real opinions to be used in the training stage (Alabid & Katheeth, 2021). The labeling process is carried out to determine the category of sentiment that has been obtained, whether the sentiment is positive or negative.…”

Section: Labelingmentioning

confidence: 99%

Sentiment Analysis of Flood Disaster Management in Jakarta on Twitter Using Support Vector Machines

Saddam

Dewantara

Solichin

2023

SinkrOn

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Floods can cause negative impacts in various aspects, starting from economic, social, to health aspects. Even quoted from the site gis.bnpb.go.id, during 2022, there have been 1031 cases of flood disasters in Indonesia. Meanwhile in Jakarta, in 2022 there have been 14 cases of floods that caused hundreds of people to lose their homes. Several approaches can be taken to determine public opinion about flooding, one of which is text mining with an analysis of community sentiment. Sentiment analysis aims to determine public opinion regarding flood management in the capital city of Jakarta based on positive, neutral, and negative categories. To get the public sentiment, researchers carried out several stages, including the preprocessing stage. After obtaining public sentiment regarding flood management through the preprocessing stage, then classification is carried out based on public opinion so that it can be used by related parties for evaluation material in flood handling. In this study, the classification method used is the SVM method which is one of the supervised learning methods in machine learning. After classification, the next stage is the testing process using the K-Fold Cross Validation method. From the various sentiments obtained from Twitter data, it can be concluded that there are around 414 positive sentiments and 2464 negative sentiments related to flood handling in DKI Jakarta, while the results obtained from the test results show that the accuracy reaches 88.6%, the precision reaches 88.6% and recall reached 89.4%.

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Section: Labelingmentioning

confidence: 99%

Sentiment Analysis of Flood Disaster Management in Jakarta on Twitter Using Support Vector Machines

Saddam

Dewantara

Solichin

2023

SinkrOn

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“…The results revealed that linear SVM was given higher accuracy than Kernel SVM. Alabid, &Katheeth [9]used SVM to predict the sentiment analysis for twitter data that are related to social distancing during the COVID-19 pandemic. They used recall, F1, precision, and confusion matrix in order to evaluate the performance of the SVM algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…Finally, it was shown that increasing the training data would increase the performance of the algorithm [9].…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Chatbot for Sentiment Analysis of Covid-19 Tweets

Assayed¹,

Shaalan²,

Alkhatib³

et al. 2023

Computer Networks &Amp; Communications

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The various types of social media were increased rapidly, as people’s need to share knowledge between others. In fact, there are various types of social media apps and platforms such as Facebook, Twitter, Reddit, Instagram, and others. Twitter remains one of the most popular social application that people use for sharing their emotional states. However, this has increased particularly during the COVID-19 pandemic. In this paper, we proposed a chatbot for evaluating the sentiment analysis by using machine learning algorithms. The authors used a dataset of tweets from Kaggle’s website, and that includes 41157 tweets that are related to the COVID-19. These tweets were classified and labelled to four categories: Extremely positive, positive, neutral, negative, and extremely negative. In this study, we applied Machine Learning algorithms, Support Vector Machines (SVM), and the Naïve Bayes (NB) algorithms and accordingly, we compared the accuracy between them. In addition to that, the classifiers were evaluated and compared after changing the test split ratio. The result shows that the accuracy performance of SVM algorithm is better than Naïve Bayes algorithm, even though Naïve Bayes perform poorly with low accuracy, but it trained the data faster comparing to SVM.

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“…The success of artificial intelligence (AI) systems in such analysis depends on the availability of datasets and quality of the included data, however, and pre-processing unstructured data is important step towards acquiring efficient information from medical records using AI techniques [3], [4]. Natural language processing, AI, and machine learning (ML) all have the potential to simplify the use of unstructured data; however, it is unlikely that these systems will ever reach a point at which computers can make decisions in critical cases rather than simply supporting the humans who must make those decisions [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Classification of specialities in textual medical reports based on natural language processing and feature selection

Almuhana

Abbas

2022

IJEECS

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Nowadays, a great deal of detailed information about patients, including disease status, medication history, and side effects, is collected in an electronic format; called an electronic medical record (EMR), and the data serves as a valuable resource for further analysis, diagnosis, and treatment. The huge q uantity of detailed patient information in these medical texts produces a huge challenge in terms of processing this data efficiently, however. Machine learning (ML) algorithms, artificial intelligence techniques, and natural language processing tools can have the potential effect of simplifying unstructured data, which could positively affect medical report analysis. Natural language processing (NLP) has recently made huge advances on a variety of tasks. In this paper, an automatic system was thus produced to classify specialist consultant interactions based on patients’ medical reports. NLP was used as a pre-processing step on a dataset formed of unstructured medical reports. Feature extraction and selection methods were used to convert the textual reports into sets of features and to extract the most effective features to increase classification accuracy and reduce execution time. Various classification methods were then applied (ML perceptron, logistic regression random forest (RF), and linear support vec tor classifier (LSVC)). The highest accuracy (99.39%) was achieved in ML-perceptron classification techniques .

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Sentiment analysis of Twitter posts related to the COVID-19 vaccines

Cited by 13 publications

References 49 publications

Sentiment Analysis of Flood Disaster Management in Jakarta on Twitter Using Support Vector Machines

Sentiment Analysis of Flood Disaster Management in Jakarta on Twitter Using Support Vector Machines

Machine Learning Chatbot for Sentiment Analysis of Covid-19 Tweets

Classification of specialities in textual medical reports based on natural language processing and feature selection

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