Sentimental Analysis of COVID-19 Tweets Using Deep Learning Models

The novel coronavirus disease (COVID-19) is an ongoing pandemic with large global attention. However, spreading false news on social media sites like Twitter is creating unnecessary anxiety towards this disease. The motto behind this study is to analyses tweets by Indian netizens during the COVID-19 lockdown. The data included tweets collected on the dates between 23 March 2020 and 15 July 2020 and the text has been labelled as fear, sad, anger, and joy. Data analysis was conducted by Bidirectional Encoder Representations from Transformers (BERT) model, which is a new deep-learning model for text analysis and performance and was compared with three other models such as logistic regression (LR), support vector machines (SVM), and long-short term memory (LSTM). Accuracy for every sentiment was separately calculated. The BERT model produced 89% accuracy and the other three models produced 75%, 74.75%, and 65%, respectively. Each sentiment classification has accuracy ranging from 75.88–87.33% with a median accuracy of 79.34%, which is a relatively considerable value in text mining algorithms. Our findings present the high prevalence of keywords and associated terms among Indian tweets during COVID-19. Further, this work clarifies public opinion on pandemics and lead public health authorities for a better society.

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Indoor Environmental Quality in Dwellings and Lifestyle Behaviors during the COVID-19 Pandemic: Russian Perspective

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18115975 ◽

2021 ◽

Vol 18 (11) ◽

pp. 5975

Author(s):

Vladimir Reshetnikov ◽

Oleg Mitrokhin ◽

Elena Belova ◽

Victor Mikhailovsky ◽

Maria Mikerova ◽

...

Keyword(s):

Public Health ◽

Medical Students ◽

Environmental Quality ◽

Indoor Environmental Quality ◽

Lifestyle Behaviors ◽

Contagious Diseases ◽

Health Authorities ◽

Public Health Authorities ◽

Isolation Index ◽

Novel Coronavirus

The novel coronavirus (COVID-19) outbreak is a public health emergency of international concern, and as a response, public health authorities started enforcing preventive measures like self-isolation and social distancing. The enforcement of isolation has consequences that may affect the lifestyle-related behavior of the general population. Quarantine encompasses a range of strategies that can be used to detain, isolate, or conditionally release individuals or populations infected or exposed to contagious diseases and should be tailored to circumstances. Interestingly, medical students may represent an example of how the COVID-19 pandemic can form new habits and change lifestyle behaviors. We conducted a web-based survey to assess changes in lifestyle-related behavior of self-isolated medical students during the COVID-19 pandemic. Then we analyzed the sanitary-hygienic regulations of the Russian Federation to determine the requirements for healthy buildings. Results showed that during the pandemic, the enforcement of isolation affects medical students’ lifestyle-related behavior and accompanies an increase in non-communicable diseases (NCDs). Indoor environmental quality (IEQ) and healthy buildings are cutting-edge factors in preventing COVID-19 and NCDs. The Russian sanitary-hygienic regulations support improving this factor with suitable requirements for ventilation, sewage, waste management, and disinfection. Herein, assessing isolation is possible through the hygienic self-isolation index.

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PlncRNA-HDeep: plant long noncoding RNA prediction using hybrid deep learning based on two encoding styles

BMC Bioinformatics ◽

10.1186/s12859-020-03870-2 ◽

2021 ◽

Vol 22 (S3) ◽

Author(s):

Jun Meng ◽

Qiang Kang ◽

Zheng Chang ◽

Yushi Luan

Keyword(s):

Deep Learning ◽

Noncoding Rna ◽

Nearest Neighbor ◽

Short Term Memory ◽

Biological Activities ◽

Support Vector ◽

Multiple Perspectives ◽

K Nearest Neighbor ◽

Rna Sequences ◽

Deep Learning Model

Abstract Background Long noncoding RNAs (lncRNAs) play an important role in regulating biological activities and their prediction is significant for exploring biological processes. Long short-term memory (LSTM) and convolutional neural network (CNN) can automatically extract and learn the abstract information from the encoded RNA sequences to avoid complex feature engineering. An ensemble model learns the information from multiple perspectives and shows better performance than a single model. It is feasible and interesting that the RNA sequence is considered as sentence and image to train LSTM and CNN respectively, and then the trained models are hybridized to predict lncRNAs. Up to present, there are various predictors for lncRNAs, but few of them are proposed for plant. A reliable and powerful predictor for plant lncRNAs is necessary. Results To boost the performance of predicting lncRNAs, this paper proposes a hybrid deep learning model based on two encoding styles (PlncRNA-HDeep), which does not require prior knowledge and only uses RNA sequences to train the models for predicting plant lncRNAs. It not only learns the diversified information from RNA sequences encoded by p-nucleotide and one-hot encodings, but also takes advantages of lncRNA-LSTM proposed in our previous study and CNN. The parameters are adjusted and three hybrid strategies are tested to maximize its performance. Experiment results show that PlncRNA-HDeep is more effective than lncRNA-LSTM and CNN and obtains 97.9% sensitivity, 95.1% precision, 96.5% accuracy and 96.5% F1 score on Zea mays dataset which are better than those of several shallow machine learning methods (support vector machine, random forest, k-nearest neighbor, decision tree, naive Bayes and logistic regression) and some existing tools (CNCI, PLEK, CPC2, LncADeep and lncRNAnet). Conclusions PlncRNA-HDeep is feasible and obtains the credible predictive results. It may also provide valuable references for other related research.

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Deep Learning Methods for Classification of Certain Abnormalities in Echocardiography

Electronics ◽

10.3390/electronics10040495 ◽

2021 ◽

Vol 10 (4) ◽

pp. 495

Author(s):

Imayanmosha Wahlang ◽

Arnab Kumar Maji ◽

Goutam Saha ◽

Prasun Chakrabarti ◽

Michal Jasinski ◽

...

Keyword(s):

Deep Learning ◽

Short Term Memory ◽

Support Vector ◽

Variational Autoencoder ◽

Different Types ◽

Static Images ◽

Long Short Term Memory ◽

2D And 3D ◽

Better Than

This article experiments with deep learning methodologies in echocardiogram (echo), a promising and vigorously researched technique in the preponderance field. This paper involves two different kinds of classification in the echo. Firstly, classification into normal (absence of abnormalities) or abnormal (presence of abnormalities) has been done, using 2D echo images, 3D Doppler images, and videographic images. Secondly, based on different types of regurgitation, namely, Mitral Regurgitation (MR), Aortic Regurgitation (AR), Tricuspid Regurgitation (TR), and a combination of the three types of regurgitation are classified using videographic echo images. Two deep-learning methodologies are used for these purposes, a Recurrent Neural Network (RNN) based methodology (Long Short Term Memory (LSTM)) and an Autoencoder based methodology (Variational AutoEncoder (VAE)). The use of videographic images distinguished this work from the existing work using SVM (Support Vector Machine) and also application of deep-learning methodologies is the first of many in this particular field. It was found that deep-learning methodologies perform better than SVM methodology in normal or abnormal classification. Overall, VAE performs better in 2D and 3D Doppler images (static images) while LSTM performs better in the case of videographic images.

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An Electronic Component Recognition Algorithm Based on Deep Learning with a Faster SqueezeNet

Mathematical Problems in Engineering ◽

10.1155/2020/2940286 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11

Author(s):

Yuanyuan Xu ◽

Genke Yang ◽

Jiliang Luo ◽

Jianan He

Keyword(s):

Deep Learning ◽

Characteristic Curve ◽

Area Under The Curve ◽

Principal Component ◽

Recognition Algorithm ◽

Electronic Component ◽

Support Vector ◽

Learning Networks ◽

Component Recognition ◽

And Performance

Electronic component recognition plays an important role in industrial production, electronic manufacturing, and testing. In order to address the problem of the low recognition recall and accuracy of traditional image recognition technologies (such as principal component analysis (PCA) and support vector machine (SVM)), this paper selects multiple deep learning networks for testing and optimizes the SqueezeNet network. The paper then presents an electronic component recognition algorithm based on the Faster SqueezeNet network. This structure can reduce the size of network parameters and computational complexity without deteriorating the performance of the network. The results show that the proposed algorithm performs well, where the Receiver Operating Characteristic Curve (ROC) and Area Under the Curve (AUC), capacitor and inductor, reach 1.0. When the FPR is less than or equal 10 − 6 level, the TPR is greater than or equal to 0.99; its reasoning time is about 2.67 ms, achieving the industrial application level in terms of time consumption and performance.

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A semiautomatic annotation approach for sentiment analysis

Journal of Information Science ◽

10.1177/01655515211006594 ◽

2021 ◽

pp. 016555152110065

Author(s):

Rahma Alahmary ◽

Hmood Al-Dossari

Keyword(s):

Deep Learning ◽

Sentiment Analysis ◽

Short Term Memory ◽

Machine Learning Algorithms ◽

Support Vector ◽

Short Term ◽

Term Memory ◽

Annotation Process ◽

Learning Classifiers ◽

Long Short Term Memory

Sentiment analysis (SA) aims to extract users’ opinions automatically from their posts and comments. Almost all prior works have used machine learning algorithms. Recently, SA research has shown promising performance in using the deep learning approach. However, deep learning is greedy and requires large datasets to learn, so it takes more time for data annotation. In this research, we proposed a semiautomatic approach using Naïve Bayes (NB) to annotate a new dataset in order to reduce the human effort and time spent on the annotation process. We created a dataset for the purpose of training and testing the classifier by collecting Saudi dialect tweets. The dataset produced from the semiautomatic model was then used to train and test deep learning classifiers to perform Saudi dialect SA. The accuracy achieved by the NB classifier was 83%. The trained semiautomatic model was used to annotate the new dataset before it was fed into the deep learning classifiers. The three deep learning classifiers tested in this research were convolutional neural network (CNN), long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM). Support vector machine (SVM) was used as the baseline for comparison. Overall, the performance of the deep learning classifiers exceeded that of SVM. The results showed that CNN reported the highest performance. On one hand, the performance of Bi-LSTM was higher than that of LSTM and SVM, and, on the other hand, the performance of LSTM was higher than that of SVM. The proposed semiautomatic annotation approach is usable and promising to increase speed and save time and effort in the annotation process.

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Comparative analysis of Kernel-based versus BFGS-ANN and deep learning methods in monthly reference evaporation estimation

10.5194/hess-2020-224 ◽

2020 ◽

Author(s):

Mohammad Taghi Sattari ◽

Halit Apaydin ◽

Shahab Shamshirband ◽

Amir Mosavi

Keyword(s):

Deep Learning ◽

Minimum Temperature ◽

Meteorological Parameters ◽

Short Term Memory ◽

Sunshine Duration ◽

Gaussian Process Regression ◽

Support Vector ◽

Ann Model ◽

Learning Methods ◽

Average Maximum

Abstract. Proper estimation of the reference evapotranspiration (ET0) amount is an indispensable matter for agricultural water management in the efficient use of water. The aim of study is to estimate the amount of ET0 with a different machine and deep learning methods by using minimum meteorological parameters in the Corum region which is an arid and semi-arid climate with an important agricultural center of Turkey. In this context, meteorological variables of average, maximum and minimum temperature, sunshine duration, wind speed, average, maximum, and minimum relative humidity are used as input data monthly. Two different kernel-based (Gaussian Process Regression (GPR) and Support Vector Regression (SVR)) methods, BFGS-ANN and Long short-term memory models were used to estimate ET0 amounts in 10 different combinations. According to the results obtained, all four methods used predicted ET0 amounts in acceptable accuracy and error levels. BFGS-ANN model showed higher success than the others. In kernel-based GPR and SVR methods, Pearson VII function-based universal kernel was the most successful kernel function. Besides, the scenario that is related to temperature in all scenarios used, including average temperature, maximum and minimum temperature, and sunshine duration gave the best results. The second-best scenario was the one that covers only the sunshine duration. In this case, the ANN (BFGS-ANN) model, which is optimized with the BFGS method that uses only the sunshine duration, can be estimated with the 0.971 correlation coefficient of ET0 without the need for other meteorological parameters.

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The Impact of Covid-19 Public Health Policies on Persons with Disability and their Caregivers in Malta

International Journal of Social Science Research and Review ◽

10.47814/ijssrr.v4i2.88 ◽

2021 ◽

Vol 4 (2) ◽

pp. 25-37

Author(s):

Andrew Camilleri ◽

Samantha Pace Gasan ◽

Andrew Azzopardi

Keyword(s):

Public Health ◽

World Health Organisation ◽

World Health ◽

Persons With Disabilities ◽

Health Authorities ◽

Essential Services ◽

Public Health Authorities ◽

Novel Coronavirus ◽

Restrictive Measures ◽

The Impact

On March 11, 2020, the World Health Organisation (WHO) declared a global health pandemic, due to the spread of a novel coronavirus, later named “Covid-19”. The spread of Covid-19 led to social isolation, distancing and a number of restrictive measures in Malta. The aim of this paper is to analyse the impact of Covid-19 and the subsequent restrictive measures on persons with disability and their caregivers and families in Malta. Using thematic analysis, the study found that a variety of impacts ranging from a sense of isolation, lack of essential services being provided, additional difficulties encountered at the place of work and education and measures that were not sufficiently tailored for persons with disability issued by public health authorities. Underlying the additional difficulties brought about by Covid-19, structural difficulties to access essential services as well as ignorance from policy makers and politicians and the added “vulnerable-ization” of persons with disabilities were found to be highly impacting factors that pervade the experience of persons with disabilities and their caregivers.

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SARS-CoV-2 and Coronavirus Disease 2019: What We Know So Far

Pathogens ◽

10.3390/pathogens9030231 ◽

2020 ◽

Vol 9 (3) ◽

pp. 231 ◽

Cited By ~ 125

Author(s):

Firas A. Rabi ◽

Mazhar S. Al Zoubi ◽

Ghena A. Kasasbeh ◽

Dunia M. Salameh ◽

Amjad D. Al-Nasser

Keyword(s):

Vaccine Development ◽

Current Knowledge ◽

Treatment Options ◽

The Novel ◽

Clinical Criteria ◽

Health Authorities ◽

Close Relationship ◽

Public Health Authorities ◽

Molecular Information ◽

Novel Coronavirus

In December 2019, a cluster of fatal pneumonia cases presented in Wuhan, China. They were caused by a previously unknown coronavirus. All patients had been associated with the Wuhan Wholefood market, where seafood and live animals are sold. The virus spread rapidly and public health authorities in China initiated a containment effort. However, by that time, travelers had carried the virus to many countries, sparking memories of the previous coronavirus epidemics, severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), and causing widespread media attention and panic. Based on clinical criteria and available serological and molecular information, the new disease was called coronavirus disease of 2019 (COVID-19), and the novel coronavirus was called SARS Coronavirus-2 (SARS-CoV-2), emphasizing its close relationship to the 2002 SARS virus (SARS-CoV). The scientific community raced to uncover the origin of the virus, understand the pathogenesis of the disease, develop treatment options, define the risk factors, and work on vaccine development. Here we present a summary of current knowledge regarding the novel coronavirus and the disease it causes.

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Sentiment Analysis of Lithuanian Texts Using Traditional and Deep Learning Approaches

Computers ◽

10.3390/computers8010004 ◽

2019 ◽

Vol 8 (1) ◽

pp. 4 ◽

Cited By ~ 4

Author(s):

Jurgita Kapočiūtė-Dzikienė ◽

Robertas Damaševičius ◽

Marcin Woźniak

Keyword(s):

Machine Learning ◽

Deep Learning ◽

Sentiment Analysis ◽

Short Term Memory ◽

Machine Learning Techniques ◽

Support Vector ◽

Learning Approaches ◽

Full Dataset ◽

Learning Techniques ◽

Long Short Term Memory

We describe the sentiment analysis experiments that were performed on the Lithuanian Internet comment dataset using traditional machine learning (Naïve Bayes Multinomial—NBM and Support Vector Machine—SVM) and deep learning (Long Short-Term Memory—LSTM and Convolutional Neural Network—CNN) approaches. The traditional machine learning techniques were used with the features based on the lexical, morphological, and character information. The deep learning approaches were applied on the top of two types of word embeddings (Vord2Vec continuous bag-of-words with negative sampling and FastText). Both traditional and deep learning approaches had to solve the positive/negative/neutral sentiment classification task on the balanced and full dataset versions. The best deep learning results (reaching 0.706 of accuracy) were achieved on the full dataset with CNN applied on top of the FastText embeddings, replaced emoticons, and eliminated diacritics. The traditional machine learning approaches demonstrated the best performance (0.735 of accuracy) on the full dataset with the NBM method, replaced emoticons, restored diacritics, and lemma unigrams as features. Although traditional machine learning approaches were superior when compared to the deep learning methods; deep learning demonstrated good results when applied on the small datasets.

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An interim review of the epidemiological characteristics of 2019 novel coronavirus

Epidemiology and Health ◽

10.4178/epih.e2020006 ◽

2020 ◽

Vol 42 ◽

pp. e2020006 ◽

Cited By ~ 59

Author(s):

Sukhyun Ryu ◽

Byung Chul Chun

Keyword(s):

Public Health ◽

Public Health Emergency ◽

Control Measures ◽

Health Authorities ◽

Public Health Authorities ◽

Novel Coronavirus ◽

Epidemiological Characteristics

OBJECTIVES: The 2019 novel coronavirus (2019-nCoV) from Wuhan, China is currently recognized as a public health emergency of global concern.METHODS: We reviewed the currently available literature to provide up-to-date guidance on control measures to be implemented by public health authorities.RESULTS: Some of the epidemiological characteristics of 2019-nCoV have been identified. However, there remain considerable uncertainties, which should be considered when providing guidance to public health authorities on control measures.CONCLUSIONS: Additional studies incorporating more detailed information from confirmed cases would be valuable.

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