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Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers-A study to show how popularity is affecting accuracy in social media.

In Applied soft computing

COVID-19 originally known as Corona VIrus Disease of 2019, has been declared as a pandemic by World Health Organization (WHO) on 11th March 2020. Unprecedented pressures have mounted on each country to make compelling requisites for controlling the population by assessing the cases and properly utilizing available resources. The rapid number of exponential cases globally has become the apprehension of panic, fear and anxiety among people. The mental and physical health of the global population is found to be directly proportional to this pandemic disease. The current situation has reported more than twenty four million people being tested positive worldwide as of 27th August, 2020 Therefore, it's the need of the hour to implement different measures to safeguard the countries by demystifying the pertinent facts and information. This paper aims to bring out the fact that tweets containing all handles related to COVID-19 and WHO have been unsuccessful in guiding people around this pandemic outbreak appositely. This study analyses two types of tweets gathered during the pandemic times. In one case, around twenty three thousand most re-tweeted tweets within the time span from1st Jan 2019 to 23rd March 2020 have been analysed and observation says that the maximum number of the tweets portrays neutral or negative sentiments. On the other hand, a dataset containing 226668 tweets collected within the time span between December 2019 and May 2020 have been analysed which contrastingly show that there were a maximum number of positive and neutral tweets tweeted by netizens. The research demonstrates that though people have tweeted mostly positive regarding COVID-19, yet netizens were busy engrossed in re-tweeting the negative tweets and that no useful words could be found in WordCloud or computations using word frequency in tweets. The claims have been validated through a proposed model using deep learning classifiers with admissible accuracy up to 81%. Apart from these the authors have proposed the implementation of a Gaussian membership function based fuzzy rule base to correctly identify sentiments from tweets. The accuracy for the said model yields up to a permissible rate of 79%.

Chakraborty Koyel, Bhatia Surbhi, Bhattacharyya Siddhartha, Platos Jan, Bag Rajib, Hassanien Aboul Ella

2020-Sep-28

00-01, 99-00, COVID-19, Deep learning, Emotional intelligence, Fuzzy rule, Gaussian membership function, Sentiment analysis, Tweets, WHO

General General

Health is the Motive and Digital is the Instrument.

In Journal of the Indian Institute of Science

The coronavirus crisis has seen an unprecedented response from India and the world. If the viral outbreak has exposed gross inadequacies in the healthcare systems of nations both rich and poor, it has stirred a digital healthcare revolution that has been building since the past decade. We have seen how this new era of digital health evolved over the years since healthcare started getting increasingly unaffordable in the western countries forcing a relook in their strategies to explosion of digital innovations in mobile telephony and applications, internet, wearable devices, artificial intelligence, robotics, big data and genomics. The single biggest trigger for the digital shift has indeed been the COVID-19 pandemic this year, more so in India with astonishing response from the private enterprise and the proactive push from the government so evident. However, the full potential of this digital revolution cannot be realized as long as core structural reforms in public healthcare do not take place along with significant boost in digital infrastructure. The way digital technologies have helped facilitate strategy and response to the global pandemic and with predictions of more zoonotic outbreaks impending in the coming years, it has become imperative for the world to increasingly adopt and integrate digital innovations to make healthcare more accessible, interconnected and affordable.

Seethalakshmi S, Nandan Rahul

2020-Sep-27

General General

COVID-19 and Media Datasets: Period- and location-specific textual data mining.

In Data in brief

The vocabulary used in news on a disease such as COVID-19 changes according the period [4]. This aspect is discussed on the basis of MEDISYS-sourced media datasets via two studies. The first focuses on terminology extraction and the second on period prediction according to the textual content using machine learning approaches.

Roche Mathieu

2020-Sep-30

COVID-19, Classification, Corpus, NLP, Terminology Extraction, Text-Mining

General General

Automated classification of movement quality using the Microsoft Kinect V2 sensor.

In Computers in biology and medicine

Practitioners commonly perform movement quality assessment through qualitative assessment protocols, which can be time-intensive and prone to inter-rater measurement bias. The advent of portable and inexpensive marker-less motion capture systems can improve assessment through objective joint kinematic analysis. The current study aimed to evaluate various machine learning models that used kinematic features from Kinect position data to classify a performer's Movement Competency Screen (MCS) score. A Kinect V2 sensor collected position data from 31 physically active males as they performed bilateral squat, forward lunge, and single-leg squat; and the movement quality was rated according to the MCS criteria. Features were extracted and selected from domain knowledge-based kinematic variables as model input. Multiclass logistic regression (MLR) was then performed to translate joint kinematics into MCS score. Performance indicators were calculated after a 10-fold cross validation of each model developed from Kinect-based kinematic variables. The analyses revealed that the models' sensitivity, specificity, and accuracy ranged from 0.66 to 0.89, 0.58 to 0.86, and 0.74 to 0.85, respectively. In conclusion, the Kinect-based automated movement quality assessment is a suitable, novel, and practical approach to movement quality assessment.

Dajime Peter Fermin, Smith Heather, Zhang Yanxin

2020-Sep-29

Intelligent system, Kinect, Kinematics, Machine learning, Movement quality assessment

General General

DeepAdd: Protein function prediction from k-mer embedding and additional features.

In Computational biology and chemistry

With the application of new high throughput sequencing technology, a large number of protein sequences is becoming available. Determination of the functional characteristics of these proteins by experiments is an expensive endeavor that requires a lot of time. Furthermore, at the organismal level, such kind of experimental functional analyses can be conducted only for a very few selected model organisms. Computational function prediction methods can be used to fill this gap. The functions of proteins are classified by Gene Ontology (GO), which contains more than 40,000 classifications in three domains, Molecular Function (MF), Biological Process (BP), and Cellular Component (CC). Additionally, since proteins have many functions, function prediction represents a multi-label and multi-class problem. We developed a new method to predict protein function from sequence. To this end, natural language model was used to generate word embedding of sequence and learn features from it by deep learning, and additional features to locate every protein. Our method uses the dependencies between GO classes as background information to construct a deep learning model. We evaluate our method using the standards established by the Computational Assessment of Function Annotation (CAFA) and have noticeable improvement over several algorithms, such as FFPred, DeepGO, GoFDR and other methods compared on the CAFA3 datasets.

Du Zhihua, He Yufeng, Li Jianqiang, Uversky Vladimir N

2020-Sep-23

Convolution neural network, Natural language process, Protein function prediction, Protein-protein interaction network, Sequence similarity profile

General General

Multifaceted impulsivity as a moderator of social anxiety and cannabis use during pregaming.

In Journal of anxiety disorders

Individuals may drink or use cannabis to cope with social anxiety, and drinking or using cannabis prior to social situations (e.g., pregaming) may be a way to limit the experience of anxiety when entering social settings. However, theoretical and empirical work has reported mixed associations between social anxiety and substance use, specifically alcohol and cannabis. Little work has looked at how other variables, such as impulsivity (a central component to high risk drinking such as pregaming), may shed light onto these mixed findings. College students who reported past year pregaming (n = 363) completed self-report surveys. Supporting prior work, we found that social anxiety was associated with fewer pregaming days, even among those high in sensation seeking. However, those reporting higher social anxiety also reported higher cannabis use during pregaming, specifically among those who reported high sensation seeking and high positive urgency. Results suggest specific facets of impulsivity may affect the association between social anxiety and cannabis use during high risk drinking events.

Davis Jordan P, Christie Nina C, Pakdaman Sheila, Hummer Justin F, DeLeon Jessenia, Clapp John D, Pedersen Eric R

2020-Sep-28

Anxiety disorders, College students, Heavy drinking, Impulsivity, Substance use disorder, Young adults