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Tian Y, Zhang W, Duan L, McDonald W, Osgood N. Comparison of pretrained transformer-based models for influenza and COVID-19 detection using social media text data in Saskatchewan, Canada. Front Digit Health 2023; 5:1203874. [PMID: 37448834 PMCID: PMC10338115 DOI: 10.3389/fdgth.2023.1203874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Accepted: 06/02/2023] [Indexed: 07/15/2023] Open
Abstract
Background The use of social media data provides an opportunity to complement traditional influenza and COVID-19 surveillance methods for the detection and control of outbreaks and informing public health interventions. Objective The first aim of this study is to investigate the degree to which Twitter users disclose health experiences related to influenza and COVID-19 that could be indicative of recent plausible influenza cases or symptomatic COVID-19 infections. Second, we seek to use the Twitter datasets to train and evaluate the classification performance of Bidirectional Encoder Representations from Transformers (BERT) and variant language models in the context of influenza and COVID-19 infection detection. Methods We constructed two Twitter datasets using a keyword-based filtering approach on English-language tweets collected from December 2016 to December 2022 in Saskatchewan, Canada. The influenza-related dataset comprised tweets filtered with influenza-related keywords from December 13, 2016, to March 17, 2018, while the COVID-19 dataset comprised tweets filtered with COVID-19 symptom-related keywords from January 1, 2020, to June 22, 2021. The Twitter datasets were cleaned, and each tweet was annotated by at least two annotators as to whether it suggested recent plausible influenza cases or symptomatic COVID-19 cases. We then assessed the classification performance of pre-trained transformer-based language models, including BERT-base, BERT-large, RoBERTa-base, RoBERT-large, BERTweet-base, BERTweet-covid-base, BERTweet-large, and COVID-Twitter-BERT (CT-BERT) models, on each dataset. To address the notable class imbalance, we experimented with both oversampling and undersampling methods. Results The influenza dataset had 1129 out of 6444 (17.5%) tweets annotated as suggesting recent plausible influenza cases. The COVID-19 dataset had 924 out of 11939 (7.7%) tweets annotated as inferring recent plausible COVID-19 cases. When compared against other language models on the COVID-19 dataset, CT-BERT performed the best, supporting the highest scores for recall (94.8%), F1(94.4%), and accuracy (94.6%). For the influenza dataset, BERTweet models exhibited better performance. Our results also showed that applying data balancing techniques such as oversampling or undersampling method did not lead to improved model performance. Conclusions Utilizing domain-specific language models for monitoring users' health experiences related to influenza and COVID-19 on social media shows improved classification performance and has the potential to supplement real-time disease surveillance.
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Affiliation(s)
| | | | | | | | - Nathaniel Osgood
- Department of Computer Science, University of Saskatchewan, Saskatoon, SK, Canada
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Qorib M, Oladunni T, Denis M, Ososanya E, Cotae P. COVID-19 Vaccine Hesitancy: A Global Public Health and Risk Modelling Framework Using an Environmental Deep Neural Network, Sentiment Classification with Text Mining and Emotional Reactions from COVID-19 Vaccination Tweets. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:ijerph20105803. [PMID: 37239532 DOI: 10.3390/ijerph20105803] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 03/31/2023] [Accepted: 04/04/2023] [Indexed: 05/28/2023]
Abstract
Popular social media platforms, such as Twitter, have become an excellent source of information with their swift information dissemination. Individuals with different backgrounds convey their opinions through social media platforms. Consequently, these platforms have become a profound instrument for collecting enormous datasets. We believe that compiling, organizing, exploring, and analyzing data from social media platforms, such as Twitter, can offer various perspectives to public health organizations and decision makers in identifying factors that contribute to vaccine hesitancy. In this study, public tweets were downloaded daily from Tweeter using the Tweeter API. Before performing computation, the tweets were preprocessed and labeled. Vocabulary normalization was based on stemming and lemmatization. The NRCLexicon technique was deployed to convert the tweets into ten classes: positive sentiment, negative sentiment, and eight basic emotions (joy, trust, fear, surprise, anticipation, anger, disgust, and sadness). t-test was used to check the statistical significance of the relationships among the basic emotions. Our analysis shows that the p-values of joy-sadness, trust-disgust, fear-anger, surprise-anticipation, and negative-positive relations are close to zero. Finally, neural network architectures, including 1DCNN, LSTM, Multiple-Layer Perceptron, and BERT, were trained and tested in a COVID-19 multi-classification of sentiments and emotions (positive, negative, joy, sadness, trust, disgust, fear, anger, surprise, and anticipation). Our experiment attained an accuracy of 88.6% for 1DCNN at 1744 s, 89.93% accuracy for LSTM at 27,597 s, while MLP achieved an accuracy of 84.78% at 203 s. The study results show that the BERT model performed the best, with an accuracy of 96.71% at 8429 s.
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Affiliation(s)
- Miftahul Qorib
- Department of Computer Science and Information Technology, University of the District of Columbia, Washington, DC 20008, USA
- Department of Mathematics and Statistics, University of the District of Columbia, Washington, DC 20008, USA
| | - Timothy Oladunni
- Department of Computer Science, Morgan State University, Baltimore, MD 21251, USA
| | - Max Denis
- Department of Mechanical and Biomedical Engineering, University of the District of Columbia, Washington, DC 20008, USA
| | - Esther Ososanya
- Department of Electrical and Computer Engineering, University of the District of Columbia, Washington, DC 20008, USA
| | - Paul Cotae
- Department of Electrical and Computer Engineering, University of the District of Columbia, Washington, DC 20008, USA
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Arbane M, Benlamri R, Brik Y, Alahmar AD. Social media-based COVID-19 sentiment classification model using Bi-LSTM. EXPERT SYSTEMS WITH APPLICATIONS 2023; 212:118710. [PMID: 36060151 PMCID: PMC9425711 DOI: 10.1016/j.eswa.2022.118710] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 06/26/2022] [Accepted: 08/25/2022] [Indexed: 06/15/2023]
Abstract
Internet public social media and forums provide a convenient channel for people concerned about public health issues, such as COVID-19, to share and discuss information/misinformation with each other. In this paper, we propose a natural language processing (NLP) method based on Bidirectional Long Short-Term Memory (Bi-LSTM) technique to perform sentiment classification and uncover various issues related to COVID-19 public opinions. Bi-LSTM is an improved version of conventional LSTMs for generating the output from both left and right contexts at each time step. We experimented with real datasets extracted from Twitter and Reddit social media platforms, and our experimental results showed improved metrics compared with the conventional LSTM model as well as recent studies available in the literature. The proposed model can be used by official institutions to mitigate the effects of negative messages and to understand peoples' concerns during the pandemic. Furthermore, our findings shed light on the importance of using NLP techniques to analyze public opinion and to combat the spreading of misinformation and to guide health decision-making.
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Affiliation(s)
- Mohamed Arbane
- LASS Laboratory, Mohamed Boudiaf University, M'sila, 28000, Algeria
| | - Rachid Benlamri
- University of Doha for Science and Technology, Doha, PO Box 24449, Qatar
| | - Youcef Brik
- LASS Laboratory, Mohamed Boudiaf University, M'sila, 28000, Algeria
| | - Ayman Diyab Alahmar
- Department of Software Engineering, Lakehead University, Thunder Bay, P7B 5E1, Ontario, Canada
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4
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Sun H, Ren G, Teng X, Song L, Li K, Yang J, Hu X, Zhan Y, Wan SBN, Wong MFE, Chan KK, Tsang HCH, Xu L, Wu TC, Kong FM(S, Wang YXJ, Qin J, Chan WCL, Ying M, Cai J. Artificial intelligence-assisted multistrategy image enhancement of chest X-rays for COVID-19 classification. Quant Imaging Med Surg 2023; 13:394-416. [PMID: 36620146 PMCID: PMC9816729 DOI: 10.21037/qims-22-610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Accepted: 09/17/2022] [Indexed: 11/13/2022]
Abstract
Background The coronavirus disease 2019 (COVID-19) led to a dramatic increase in the number of cases of patients with pneumonia worldwide. In this study, we aimed to develop an AI-assisted multistrategy image enhancement technique for chest X-ray (CXR) images to improve the accuracy of COVID-19 classification. Methods Our new classification strategy consisted of 3 parts. First, the improved U-Net model with a variational encoder segmented the lung region in the CXR images processed by histogram equalization. Second, the residual net (ResNet) model with multidilated-rate convolution layers was used to suppress the bone signals in the 217 lung-only CXR images. A total of 80% of the available data were allocated for training and validation. The other 20% of the remaining data were used for testing. The enhanced CXR images containing only soft tissue information were obtained. Third, the neural network model with a residual cascade was used for the super-resolution reconstruction of low-resolution bone-suppressed CXR images. The training and testing data consisted of 1,200 and 100 CXR images, respectively. To evaluate the new strategy, improved visual geometry group (VGG)-16 and ResNet-18 models were used for the COVID-19 classification task of 2,767 CXR images. The accuracy of the multistrategy enhanced CXR images was verified through comparative experiments with various enhancement images. In terms of quantitative verification, 8-fold cross-validation was performed on the bone suppression model. In terms of evaluating the COVID-19 classification, the CXR images obtained by the improved method were used to train 2 classification models. Results Compared with other methods, the CXR images obtained based on the proposed model had better performance in the metrics of peak signal-to-noise ratio and root mean square error. The super-resolution CXR images of bone suppression obtained based on the neural network model were also anatomically close to the real CXR images. Compared with the initial CXR images, the classification accuracy rates of the internal and external testing data on the VGG-16 model increased by 5.09% and 12.81%, respectively, while the values increased by 3.51% and 18.20%, respectively, for the ResNet-18 model. The numerical results were better than those of the single-enhancement, double-enhancement, and no-enhancement CXR images. Conclusions The multistrategy enhanced CXR images can help to classify COVID-19 more accurately than the other existing methods.
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Affiliation(s)
- Hongfei Sun
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
- School of Automation, Northwestern Polytechnical University, Xi’an, China
| | - Ge Ren
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Xinzhi Teng
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Liming Song
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Kang Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jianhua Yang
- School of Automation, Northwestern Polytechnical University, Xi’an, China
| | - Xiaofei Hu
- Department of Radiology, Southwest Hospital, Third Military Medical University (Army Medical University), Chongqing, China
| | - Yuefu Zhan
- Department of Radiology, Hainan Women and Children’s Medical Center, Hainan, China
| | - Shiu Bun Nelson Wan
- Department of Radiology, Pamela Youde Nethersole Eastern Hospital, Hong Kong, China
| | - Man Fung Esther Wong
- Department of Radiology, Pamela Youde Nethersole Eastern Hospital, Hong Kong, China
| | - King Kwong Chan
- Department of Radiology and Imaging, Queen Elizabeth Hospital, Hong Kong, China
| | | | - Lu Xu
- Department of Radiology and Imaging, Queen Elizabeth Hospital, Hong Kong, China
| | - Tak Chiu Wu
- Department of Medicine, Queen Elizabeth Hospital, Hong Kong, China
| | | | - Yi Xiang J. Wang
- Deparment of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Hong Kong, China
| | - Jing Qin
- School of Nursing, The Hong Kong Polytechnic University, Hong Kong, China
| | - Wing Chi Lawrence Chan
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Michael Ying
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
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Deep SVDD and Transfer Learning for COVID-19 Diagnosis Using CT Images. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023; 2023:6070970. [PMID: 36926185 PMCID: PMC10014155 DOI: 10.1155/2023/6070970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 01/25/2023] [Accepted: 02/06/2023] [Indexed: 03/09/2023]
Abstract
The novel coronavirus disease (COVID-19), which appeared in Wuhan, China, is spreading rapidly worldwide. Health systems in many countries have collapsed as a result of this pandemic, and hundreds of thousands of people have died due to acute respiratory distress syndrome caused by this virus. As a result, diagnosing COVID-19 in the early stages of infection is critical in the fight against the disease because it saves the patient's life and prevents the disease from spreading. In this study, we proposed a novel approach based on transfer learning and deep support vector data description (DSVDD) to distinguish among COVID-19, non-COVID-19 pneumonia, and intact CT images. Our approach consists of three models, each of which can classify one specific category as normal and the other as anomalous. To our knowledge, this is the first study to use the one-class DSVDD and transfer learning to diagnose lung disease. For the proposed approach, we used two scenarios: one with pretrained VGG16 and one with ResNet50. The proposed models were trained using data gathered with the assistance of an expert radiologist from three internet-accessible sources in end-to-end fusion using three split data ratios. Based on training with 70%, 50%, and 30% of the data, the proposed VGG16 models achieved (0.8281, 0.9170, and 0.9294) for the F1 score, while the proposed ResNet50 models achieved (0.9109, 0.9188, and 0.9333).
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Didi Y, Walha A, Ben Halima M, Wali A. COVID-19 Outbreak Forecasting Based on Vaccine Rates and Tweets Classification. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:4535541. [PMID: 36337272 PMCID: PMC9633186 DOI: 10.1155/2022/4535541] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 07/17/2022] [Accepted: 10/05/2022] [Indexed: 09/08/2024]
Abstract
The spread of COVID-19 has affected more than 200 countries and has caused serious public health concerns. The infected cases are on the increase despite the effectiveness of the vaccines. An efficient and quick surveillance system for COVID-19 can help healthcare decision-makers to contain the virus spread. In this study, we developed a novel framework using machine learning (ML) models capable of detecting COVID-19 accurately at an early stage. To estimate the risks, many models use social networking sites (SNSs) in tracking the disease outbreak. Twitter is one of the SNSs that is widely used to create an efficient resource for disease real-time analysis and can provide an early warning for health officials. We introduced a pipeline framework of outbreak prediction that incorporates a first-step hybrid method of word embedding for tweet classification. In the second step, we considered the classified tweets with external features such as vaccine rate associated with infected cases passed to machine learning algorithms for daily predictions. Thus, we applied different machine learning models such as the SVM, RF, and LR for classification and the LSTM, Prophet, and SVR for prediction. For the hybrid word embedding techniques, we applied TF-IDF, FastText, and Glove and a combination of the three features to enhance the classification. Furthermore, to improve the forecast performance, we incorporated vaccine data as input together with tweets and confirmed cases. The models' performance is more than 80% accurate, which shows the reliability of the proposed study.
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Affiliation(s)
- Y. Didi
- Department of Computer Science, Umm Al-Qura University, Makkah 24243, Saudi Arabia
- REsearch Groups in Intelligent Machines (REGIM-Lab), National Engineering School of Sfax, University of Sfax, Sfax 3038, Tunisia
| | - A. Walha
- Department of Computer Science, Umm Al-Qura University, Makkah 24243, Saudi Arabia
- REsearch Groups in Intelligent Machines (REGIM-Lab), National Engineering School of Sfax, University of Sfax, Sfax 3038, Tunisia
| | - M. Ben Halima
- REsearch Groups in Intelligent Machines (REGIM-Lab), National Engineering School of Sfax, University of Sfax, Sfax 3038, Tunisia
| | - A. Wali
- REsearch Groups in Intelligent Machines (REGIM-Lab), National Engineering School of Sfax, University of Sfax, Sfax 3038, Tunisia
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Sitaula C, Shahi TB. Monkeypox Virus Detection Using Pre-trained Deep Learning-based Approaches. J Med Syst 2022; 46:78. [PMID: 36201085 PMCID: PMC9535233 DOI: 10.1007/s10916-022-01868-2] [Citation(s) in RCA: 38] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2022] [Accepted: 09/22/2022] [Indexed: 11/17/2022]
Abstract
Monkeypox virus is emerging slowly with the decline of COVID-19 virus infections around the world. People are afraid of it, thinking that it would appear as a pandemic like COVID-19. As such, it is crucial to detect them earlier before widespread community transmission. AI-based detection could help identify them at the early stage. In this paper, we aim to compare 13 different pre-trained deep learning (DL) models for the Monkeypox virus detection. For this, we initially fine-tune them with the addition of universal custom layers for all of them and analyse the results using four well-established measures: Precision, Recall, F1-score, and Accuracy. After the identification of the best-performing DL models, we ensemble them to improve the overall performance using a majority voting over the probabilistic outputs obtained from them. We perform our experiments on a publicly available dataset, which results in average Precision, Recall, F1-score, and Accuracy of 85.44%, 85.47%, 85.40%, and 87.13%, respectively with the help of our proposed ensemble approach. These encouraging results, which outperform the state-of-the-art methods, suggest that the proposed approach is applicable to health practitioners for mass screening.
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Affiliation(s)
- Chiranjibi Sitaula
- Department of Electrical and Computer Systems Engineering, Monash University, Wellignton Rd, Clayton, VIC, 3800, Australia.
| | - Tej Bahadur Shahi
- School of Engineering and Technology, Central Queensland University, Norman Garden, QLD, 4701, Australia.,Central Department of Computer Science and IT, Tribhuvan University, TU Rd, Kirtipur, Kathmandu, 44618, Nepal
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8
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Fattoh IE, Kamal Alsheref F, Ead WM, Youssef AM. Semantic Sentiment Classification for COVID-19 Tweets Using Universal Sentence Encoder. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:6354543. [PMID: 36248924 PMCID: PMC9556213 DOI: 10.1155/2022/6354543] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 08/30/2022] [Accepted: 09/23/2022] [Indexed: 11/17/2022]
Abstract
The spread of data on the web has increased in the last twenty years. One of the reasons is the appearance of social media. The data on social sites describe many real-life events in our daily lives. In the period of the COVID-19 pandemic, a lot of people and media organizations were writing and documenting their health status and the latest news about the coronavirus on social media. Using these tweets (sentiments) about the coronavirus and analyzing them in a computational model can help decision makers in measuring public opinion and yielding remarkable findings. In this research article, we introduce a deep learning sentiment analysis model based on Universal Sentence Encoder. The dataset used in this research was collected from Twitter, and it was classified as positive, neutral, and negative. The sentence embedding model determines the meaning of word sequences instead of individual words. The model divides the dataset into training and testing and depends on the sentence similarity in detecting sentiment class. The obtained accuracy results reached 78.062%, and this result outperforms many traditional ML classifiers based on TF-IDF applied on the same dataset and another model based on the CNN classifier.
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Affiliation(s)
- Ibrahim Eldesouky Fattoh
- Computer Science Department, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, Egypt
| | - Fahad Kamal Alsheref
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, Egypt
| | - Waleed M. Ead
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef, Egypt
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9
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Detecting Personal Medication Intake in Twitter via Domain Attention-Based RNN with Multi-Level Features. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:5467262. [PMID: 35983151 PMCID: PMC9381240 DOI: 10.1155/2022/5467262] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 07/08/2022] [Accepted: 07/13/2022] [Indexed: 11/17/2022]
Abstract
Personal medication intake detection aims to automatically detect tweets that show clear evidence of personal medication consumption. It is a research topic that has attracted considerable attention to drug safety surveillance. This task is inevitably dependent on medical domain information, and the current main model for this task does not explicitly consider domain information. To tackle this problem, we propose a domain attention mechanism for recurrent neural networks, LSTMs, with a multi-level feature representation of Twitter data. Specifically, we utilize character-level CNN to capture morphological features at the word level. Subsequently, we feed them with word embeddings into a BiLSTM to get the hidden representation of a tweet. An attention mechanism is introduced over the hidden state of the BiLSTM to attend to special medical information. Finally, a classification is performed on the weighted hidden representation of tweets. Experiments over a publicly available benchmark dataset show that our model can exploit a domain attention mechanism to consider medical information to improve performance. For example, our approach achieves a precision score of 0.708, a recall score of 0.694, and a F1 score of 0.697, which is significantly outperforming multiple strong and relevant baselines.
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10
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Investigating the Difference of Fake News Source Credibility Recognition between ANN and BERT Algorithms in Artificial Intelligence. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12157725] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Fake news permeating life through channels misleads people into disinformation. To reduce the harm of fake news and provide multiple and effective news credibility channels, the approach of linguistics is applied to a word-frequency-based ANN system and semantics-based BERT system in this study, using mainstream news as a general news dataset and content farms as a fake news dataset for the models judging news source credibility and comparing the difference in news source credibility recognition between ANN and BERT. The research findings show high similarity in the highest and lowest hit rates between the ANN system and the BERT system (Liberty Time had the highest hit rate, while ETtoday and nooho.net had the lowest hit rates). The BERT system presents a higher and more stable overall source credibility recognition rate than the ANN system (BERT 91.2% > ANN 82.75%). Recognizing news source credibility through artificial intelligence not only could effectively enhance people’s sensitivity to news sources but, in the long term, could cultivate public media literacy to achieve the synergy of fake news resistance with technology.
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11
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Sentiment Analysis: An ERNIE-BiLSTM Approach to Bullet Screen Comments. SENSORS 2022; 22:s22145223. [PMID: 35890903 PMCID: PMC9318645 DOI: 10.3390/s22145223] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 07/11/2022] [Accepted: 07/11/2022] [Indexed: 11/17/2022]
Abstract
Sentiment analysis is one of the fields of affective computing, which detects and evaluates people’s psychological states and sentiments through text analysis. It is an important application of text mining technology and is widely used to analyze comments. Bullet screen videos have become a popular way for people to interact and communicate while watching online videos. Existing studies have focused on the form, content, and function of bullet screen comments, but few have examined bullet screen comments using natural language processing. Bullet screen comments are short text messages of different lengths and ambiguous emotional information, which makes it extremely challenging in natural language processing. Hence, it is important to understand how we can use the characteristics of bullet screen comments and sentiment analysis to understand the sentiments expressed and trends in bullet screen comments. This study poses the following research question: how can one analyze the sentiments ex-pressed in bullet screen comments accurately and effectively? This study mainly proposes an ERNIE-BiLSTM approach for sentiment analysis on bullet screen comments, which provides effective and innovative thinking for the sentiment analysis of bullet screen comments. The experimental results show that the ERNIE-BiLSTM approach has a higher accuracy rate, precision rate, recall rate, and F1-score than other methods.
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12
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Similar Word Replacement Method for Improving News Commenter Analysis. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12136803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
In Korea, it is common to read and comment on news stories on portal sites. To influence public opinion, some people write comments repeatedly, some of which are similar to those posted by others. This has become a serious social issue. In our previous research, we collected approximately 2.68 million news comments posted in April 2017. We classified the political stance of each author using a deep learning model (seq2seq), and evaluated how many similar comments each user wrote, as well as how similar each comment was to those posted by other people, using the Jaccard similarity coefficient. However, as our previous model used Jaccard’s similarity only, the meaning of the comments was not considered. To solve this problem, we propose similar word replacement (SWR) using word2vec and a method to analyze the similarity between user comments and classify the political stance of each user. In this study, we showed that when our model used SWR rather than Jaccard’s similarity, its ability to detect similarity between comments increased 3.2 times, and the accuracy of political stance classification improved by 6%.
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13
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A Deep Neural Network Technique for Detecting Real-Time Drifted Twitter Spam. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12136407] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
The social network is considered a part of most user’s lives as it contains more than a billion users, which makes it a source for spammers to spread their harmful activities. Most of the recent research focuses on detecting spammers using statistical features. However, such statistical features are changed over time, and spammers can defeat all detection systems by changing their behavior and using text paraphrasing. Therefore, we propose a novel technique for spam detection using deep neural network. We combine the tweet level detection with statistical feature detection and group their results over meta-classifier to build a robust technique. Moreover, we embed our technique with initial text paraphrasing for each detected tweet spam. We train our model using different datasets: random, continuous, balanced, and imbalanced. The obtained experimental results showed that our model has promising results in terms of accuracy, precision, and time, which make it applicable to be used in social networks.
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Arabic Hate Speech Detection Using Deep Recurrent Neural Networks. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12126010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/10/2022]
Abstract
With the vast number of comments posted daily on social media and other platforms, manually monitoring internet activity for possible national security risks or cyberbullying is an impossible task. However, with recent advances in machine learning (ML), the automatic monitoring of such posts for possible national security risks and cyberbullying becomes feasible. There is still the issue of privacy on the internet; however, in this study, only the technical aspects of designing an automated system that could monitor and detect hate speech in the Arabic language were targeted, which many companies, such as Facebook, Twitter, and others, could use to prevent hate speech and cyberbullying. For this task, a unique dataset consisting of 4203 comments classified into seven categories, including content against religion, racist content, content against gender equality, violent content, offensive content, insulting/bullying content, normal positive comments, and normal negative comments, was designed. The dataset was extensively preprocessed and labeled, and its features were extracted. In addition, the use of deep recurrent neural networks (RNNs) was proposed for the classification and detection of hate speech. The proposed RNN architecture, called DRNN-2, consisted of 10 layers with 32 batch sizes and 50 iterations for the classification task. Another model consisting of five hidden layers, called DRNN-1, was used only for binary classification. Using the proposed models, a recognition rate of 99.73% was achieved for binary classification, 95.38% for the three classes of Arabic comments, and 84.14% for the seven classes of Arabic comments. This accuracy was high for the classification of a complex language, such as Arabic, into seven different classes. The achieved accuracy was higher than that of similar methods reported in the recent literature, whether for binary classification, three-class classification, or seven-class classification, as discussed in the literature review section.
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Correia F, Madureira AM, Bernardino J. Deep Neural Networks Applied to Stock Market Sentiment Analysis. SENSORS (BASEL, SWITZERLAND) 2022; 22:4409. [PMID: 35746192 PMCID: PMC9229109 DOI: 10.3390/s22124409] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Revised: 06/03/2022] [Accepted: 06/07/2022] [Indexed: 06/15/2023]
Abstract
The volume of data is growing exponentially and becoming more valuable to organizations that collect it, from e-commerce data, shipping, audio and video logs, text messages, internet search queries, stock market activity, financial transactions, the Internet of Things, and various other sources. The major challenges are related with the way to extract insights from such a rich data environment and whether Deep Learning can be successful with Big Data. To get some insight on these topics, social network data are employed as a case study on how sentiments can affect decisions in stock market environments. In this paper, we propose a generalized Deep Learning-based classification framework for Stock Market Sentiment Analysis. This work comprises the study, the development, and implementation of an automatic classification system based on Deep Learning and the validation of its adequacy and efficiency in any scenario, particularly Stock Market Sentiment Analysis. Distinct datasets and several Deep Learning approaches with different layers and embedded techniques are used, and their performances are evaluated. These developments show how Deep Learning reacts to distinct contexts. The results also give context on how different techniques with different parameter combinations react to certain types of data. Convolution obtained the best results when dealing with complex data inputs, and long short-term layers kept a memory of data, allowing inputs which are not as common to still be considered for decisions. The models that resulted from Stock Market Sentiment Analysis datasets were applied with some success to real-life problems. The best models reached accuracies of 73% in training and 69% in certain test datasets. In a simulation, a model was able to provide a Return on Investment of 4.4%. The results contribute to understanding how to process Big Data efficiently using Deep Learning and specialized hardware techniques.
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Affiliation(s)
- Filipe Correia
- Institute of Engineering of Porto (ISEP/P.PORTO), Polytechnic of Porto, Rua Dr. António Bernardino de Almeida nº 431, 4200-072 Porto, Portugal;
- Interdisciplinary Studies Research Center (ISRC), ISEP/P.PORTO, 4249-015 Porto, Portugal
| | - Ana Maria Madureira
- Institute of Engineering of Porto (ISEP/P.PORTO), Polytechnic of Porto, Rua Dr. António Bernardino de Almeida nº 431, 4200-072 Porto, Portugal;
- Interdisciplinary Studies Research Center (ISRC), ISEP/P.PORTO, 4249-015 Porto, Portugal
| | - Jorge Bernardino
- Institute of Engineering of Coimbra (ISEC), Polytechnic of Coimbra, Rua Pedro Nunes, 3030-199 Coimbra, Portugal;
- Centre of Informatics and Systems of University of Coimbra-CISUC, Polo II, Pinhal de Marrocos, 3030-290 Coimbra, Portugal
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16
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Saleh H, Mostafa S, Alharbi A, El-Sappagh S, Alkhalifah T. Heterogeneous Ensemble Deep Learning Model for Enhanced Arabic Sentiment Analysis. SENSORS 2022; 22:s22103707. [PMID: 35632116 PMCID: PMC9147256 DOI: 10.3390/s22103707] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 05/06/2022] [Accepted: 05/10/2022] [Indexed: 11/18/2022]
Abstract
Sentiment analysis was nominated as a hot research topic a decade ago for its increasing importance in analyzing the people’s opinions extracted from social media platforms. Although the Arabic language has a significant share of the content shared across social media platforms, its content’s sentiment analysis is still limited due to its complex morphological structures and the varieties of dialects. Traditional machine learning and deep neural algorithms have been used in a variety of studies to predict sentiment analysis. Therefore, a need of changing current mechanisms is required to increase the accuracy of sentiment analysis prediction. This paper proposed an optimized heterogeneous stacking ensemble model for enhancing the performance of Arabic sentiment analysis. The proposed model combines three different of pre-trained Deep Learning (DL) models: Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) in conjunction with three meta-learners Logistic Regression (LR), Random Forest (RF), and Support Vector Machine (SVM) in order to enhance model’s performance for predicting Arabic sentiment analysis. The performance of the proposed model with RNN, LSTM, GRU, and the five regular ML techniques: Decision Tree (DT), LR, K-Nearest Neighbor (KNN), RF, and Naive Bayes (NB) are compared using three benchmarks Arabic dataset. Parameters of Machine Learning (ML) and DL are optimized using Grid search and KerasTuner, respectively. Accuracy, precision, recall, and f1-score were applied to evaluate the performance of the models and validate the results. The results show that the proposed ensemble model has achieved the best performance for each dataset compared with other models.
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Affiliation(s)
- Hager Saleh
- Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada 84511, Egypt;
- Correspondence: (H.S.); (T.A.)
| | - Sherif Mostafa
- Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada 84511, Egypt;
| | - Abdullah Alharbi
- Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia;
| | - Shaker El-Sappagh
- Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt;
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt
| | - Tamim Alkhalifah
- Department of Computer, College of Science and Arts in Ar Rass, Qassim University, Buraydah 52571, Saudi Arabia
- Correspondence: (H.S.); (T.A.)
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17
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Gourisaria MK, Chandra S, Das H, Patra SS, Sahni M, Leon-Castro E, Singh V, Kumar S. Semantic Analysis and Topic Modelling of Web-Scrapped COVID-19 Tweet Corpora through Data Mining Methodologies. Healthcare (Basel) 2022; 10:healthcare10050881. [PMID: 35628018 PMCID: PMC9141192 DOI: 10.3390/healthcare10050881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Revised: 05/02/2022] [Accepted: 05/05/2022] [Indexed: 12/04/2022] Open
Abstract
The evolution of the coronavirus (COVID-19) disease took a toll on the social, healthcare, economic, and psychological prosperity of human beings. In the past couple of months, many organizations, individuals, and governments have adopted Twitter to convey their sentiments on COVID-19, the lockdown, the pandemic, and hashtags. This paper aims to analyze the psychological reactions and discourse of Twitter users related to COVID-19. In this experiment, Latent Dirichlet Allocation (LDA) has been used for topic modeling. In addition, a Bidirectional Long Short-Term Memory (BiLSTM) model and various classification techniques such as random forest, support vector machine, logistic regression, naive Bayes, decision tree, logistic regression with stochastic gradient descent optimizer, and majority voting classifier have been adapted for analyzing the polarity of sentiment. The effectiveness of the aforesaid approaches along with LDA modeling has been tested, validated, and compared with several benchmark datasets and on a newly generated dataset for analysis. To achieve better results, a dual dataset approach has been incorporated to determine the frequency of positive and negative tweets and word clouds, which helps to identify the most effective model for analyzing the corpora. The experimental result shows that the BiLSTM approach outperforms the other approaches with an accuracy of 96.7%.
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Affiliation(s)
- Mahendra Kumar Gourisaria
- School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar 751024, Odisha, India; (S.C.); (H.D.)
- Correspondence: ; Tel.: +91-993-754-0600
| | - Satish Chandra
- School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar 751024, Odisha, India; (S.C.); (H.D.)
| | - Himansu Das
- School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar 751024, Odisha, India; (S.C.); (H.D.)
| | - Sudhansu Shekhar Patra
- School of Computer Applications, KIIT Deemed to be University, Bhubaneswar 751024, Odisha, India;
| | - Manoj Sahni
- Department of Mathematics, Pandit Deendayal Energy University, Gandhinagar 382426, Gujarat, India;
| | - Ernesto Leon-Castro
- Faculty of Economics and Administrative Sciences, Universidad Católica de la Santísima Concepción, Concepción 4030000, Chile;
| | - Vijander Singh
- Department of Computer Science and Engineering, School of Computing and Information Technology, Manipal University Jaipur, Jaipur 303007, Rajasthan, India;
- ERCIM Postdoctoral Fellow, Department of ICT and Science, Norwegian University of Science and Technology, Ankeret, B-315, Ålesund, Torgarden, 7491 Trondheim, Norway
| | - Sandeep Kumar
- Department of CSE, School of Engineering and Technology, CHRIST Deemed to be University, Bengaluru 560029, Karnataka, India;
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Spam Reviews Detection in the Time of COVID-19 Pandemic: Background, Definitions, Methods and Literature Analysis. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12073634] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
During the recent COVID-19 pandemic, people were forced to stay at home to protect their own and others’ lives. As a result, remote technology is being considered more in all aspects of life. One important example of this is online reviews, where the number of reviews increased promptly in the last two years according to Statista and Rize reports. People started to depend more on these reviews as a result of the mandatory physical distance employed in all countries. With no one speaking to about products and services feedback. Reading and posting online reviews becomes an important part of discussion and decision-making, especially for individuals and organizations. However, the growth of online reviews usage also provoked an increase in spam reviews. Spam reviews can be identified as fraud, malicious and fake reviews written for the purpose of profit or publicity. A number of spam detection methods have been proposed to solve this problem. As part of this study, we outline the concepts and detection methods of spam reviews, along with their implications in the environment of online reviews. The study addresses all the spam reviews detection studies for the years 2020 and 2021. In other words, we analyze and examine all works presented during the COVID-19 situation. Then, highlight the differences between the works before and after the pandemic in terms of reviews behavior and research findings. Furthermore, nine different detection approaches have been classified in order to investigate their specific advantages, limitations, and ways to improve their performance. Additionally, a literature analysis, discussion, and future directions were also presented.
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