Detecting Dengue/Flu Infections Based on Tweets Using LSTM and Word Embedding

Amin, Samina; Uddin, Imad; Zeb, Misbah; Alarood, Ala Abdulsalam; Mahmoud, Marwan; Alkinani, Monagi H.

doi:10.1109/access.2020.3031174

Cited by 23 publications

(20 citation statements)

References 47 publications

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“…Four algorithms were applied (DT, KNN, SVM, and RF) and, for feature extraction, TF-IDF was used. e proposed methodology was evaluated on two datasets of 6000 labelled tweets on dengue and flu [10]. e results of the proposed method have been evaluated using confusion matrix performance evaluation techniques and ROC curve and their results are graphically visualized.…”

Section: Resultsmentioning

confidence: 99%

“…In this work, the benchmark dataset on dengue and flu designed by Amin et al [10] is analyzed. ey labelled 6000 tweets on dengue and flu as infected or not infected.…”

Section: Data Gatheringmentioning

confidence: 99%

“…erefore, various studies concentrate on offering alternatives to track ILI through leveraging SMA and identifying early alerts regarding infectious diseases to conduct real-time analysis. OSM applications like Twitter can be used to predict public disease outbreaks and can promote timely information [8][9][10]. With the help of OSM, it is possible to alert healthcare consultants to provide the appropriate services and monitor the epidemic.…”

Section: Introductionmentioning

confidence: 99%

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Early Detection of Seasonal Outbreaks from Twitter Data Using Machine Learning Approaches

et al. 2021

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Seasonal outbreaks have several different periods that occur primarily during winter in temperate regions, while influenza may occur throughout the year in tropical regions, triggering outbreaks more irregularly. Similarly, dengue occurs in the star of the rainy season in early May and reaches its peak in late June. Dengue and flu brought an impact on various countries in the years 2017–2019 and streaming Twitter data reveals the status of dengue and flu outbreaks in the most affected regions. This research work presents that Social Media Analysis (SMA) can be used as a detector of the epidemic outbreak and to understand the sentiment of social media users regarding various diseases. Providing awareness about seasonal outbreaks through SMA is an effective approach for researchers and healthcare responders to detect the early outbreaks. The proposed model aims to find the sentiment about the disease in tweets, and the seasonal outbreaks-related tweets are classified into two classes as disease positive and disease negative. This work proposes a machine-learning-based approach to detect dengue and flu outbreaks in social media platform Twitter, using four machine learning algorithms: Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Decision Tree (DT), with the help of Term Frequency and Inverse Document Frequency (TF-IDF). For experimental analysis, two datasets (dengue and flu) are analyzed individually. The experimental results show that the RF classifier has outperformed the comparison models in terms of improved accuracy, precision, recall, F1-measure, and Receiver Operating Characteristic (ROC) curve. The proposed work offers favorable performance with total precision, accuracy, recall, and F1-measure ranging from 84% to 88% for conventional machine learning techniques.

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Section: Resultsmentioning

confidence: 99%

“…In this work, the benchmark dataset on dengue and flu designed by Amin et al [10] is analyzed. ey labelled 6000 tweets on dengue and flu as infected or not infected.…”

Section: Data Gatheringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Early Detection of Seasonal Outbreaks from Twitter Data Using Machine Learning Approaches

et al. 2021

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“…Early warning on outbreak detection can decrease the influence of epidemic outbreaks on public health. SNSs can now be used for disease surveillance to monitor the rate of epidemic outbreaks quicker than health care specialists and health organizations [2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…The massive proliferation of COVID-19 and the coronavirus pandemic has developed a strong necessity for the exploration of reliable methods of analytical research to understand information dissemination and pandemic crisis formation in social media. Various research studies have examined epidemic outbreaks and monitored healthcare to more rapidly and efficiently obtain informed decisions from healthcare organizations using SNS data [4,9]. Therefore, emphasis is focused on suggesting techniques that would empower SNSs to track and detect early cautions relevant to pandemic outbreaks to realize a real-time analysis [4,10].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approach for COVID-19 Detection on Twitter

Amin¹,

Uddin²,

Al-Baity³

et al. 2021

Computers, Materials &Amp; Continua

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Social networking services (SNSs) provide massive data that can be a very influential source of information during pandemic outbreaks. This study shows that social media analysis can be used as a crisis detector (e.g., understanding the sentiment of social media users regarding various pandemic outbreaks). The novel Coronavirus Disease-19 (COVID-19), commonly known as coronavirus, has affected everyone worldwide in 2020. Streaming Twitter data have revealed the status of the COVID-19 outbreak in the most affected regions. This study focuses on identifying COVID-19 patients using tweets without requiring medical records to find the COVID-19 pandemic in Twitter messages (tweets). For this purpose, we propose herein an intelligent model using traditional machine learning-based approaches, such as support vector machine (SVM), logistic regression (LR), naïve Bayes (NB), random forest (RF), and decision tree (DT) with the help of the term frequency inverse document frequency (TF-IDF) to detect the COVID-19 pandemic in Twitter messages. The proposed intelligent traditional machine learning-based model classifies Twitter messages into four categories, namely, confirmed deaths, recovered, and suspected. For the experimental analysis, the tweet data on the COVID-19 pandemic are analyzed to evaluate the results of traditional machine learning approaches. A benchmark dataset for COVID-19 on Twitter messages is developed and can be used for future research studies. The experiments show that the results of the proposed approach are promising in detecting the COVID-19 pandemic in Twitter messages with overall accuracy, precision, recall, and F1 score between 70% and 80% and the confusion matrix for machine learning approaches (i.e., SVM, NB, LR, RF, and DT) with the TF-IDF feature extraction technique.

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