New Approaches to Extract Information from Posts on COVID-19 Published on Reddit

In the last two years, we have seen a huge number of debates and discussions on COVID-19 in social media. Many authors have analyzed these debates on Facebook and Twitter, while very few ones have considered Reddit. In this paper, we focus on this social network and propose three approaches to extract information from posts on COVID-19 published in it. The first performs a semi-automatic and dynamic classification of Reddit posts. The second automatically constructs virtual subreddits, each characterized by homogeneous themes. The third automatically identifies virtual communities of users with homogeneous themes. The three approaches represent an advance over the past literature. In fact, the latter lacks studies regarding classification algorithms capable of outlining the differences among the thousands of posts on COVID-19 in Reddit. Analogously, it lacks approaches able to build virtual subreddits with homogeneous topics or virtual communities of users with common interests.

COVID-19 Severity Classification Using a Hierarchical Classification Deep Learning Model

One of the most important situations in recent years has been originated by the 2019 Coronavirus disease (COVID-19). Nowadays this disease continues to cause a large number of deaths and remains one of the main diseases in the world. In this disease is very important the early detection to avoid the spread, as well as to monitor the progress of the disease in patients, and techniques of artificial intelligence (AI) is very useful for this. This is where this work comes from, trying to contribute in the study to detect infected patients. Drawing inspiration from previous work, we studied the use of deep learning models to detect COVID-19 and classify the patients with this disease. The work was divided into three phases to detect, evaluate the percentage of infection and classify patients of COVID-19. The initial stage use CNN Densenet-161 models pre-trained to detects the COVID-19 using multi-class X-Ray images (COVID-19 vs. No-Findings vs. Pneumonia), obtaining 88.00% in accuracy, 91.3% in precision, 87.33% in recall, and 89.00% in F1-score. The next stage also use CNN Densenet-161 models pre-trained to evidenced the percentage of infection COVID-19 in the different CT-scans slices belonging to a patient, obtaining in the evaluation metrics a result of 0.95 in PC, 5.14 in MAE and 8.47 in RMSE. The last stage creates a database of histograms of different patients using their lung infections and classifies them into different degrees of severity using K-Means unsupervised learning algorithms with PCA. © 2022, Springer Nature Switzerland AG.

VirusTaxo: Taxonomic classification of viruses from the genome sequence using k-mer enrichment.

Classification of viruses into their taxonomic ranks (e.g., order, family, and genus) provides a framework to organize an abundant population of viruses. Next-generation metagenomic sequencing technologies lead to a rapid increase in generating sequencing data of viruses which require bioinformatics tools to analyze the taxonomy. Many metagenomic taxonomy classifiers have been developed to study microbiomes, but it is particularly challenging to assign the taxonomy of diverse virus sequences and there is a growing need for dedicated methods to be developed that are optimized to classify virus sequences into their taxa. For taxonomic classification of viruses from metagenomic sequences, we developed VirusTaxo using diverse (e.g., 402 DNA and 280 RNA) genera of viruses. VirusTaxo has an average accuracy of 93% at genus level prediction in DNA and RNA viruses. VirusTaxo outperformed existing taxonomic classifiers of viruses where it assigned taxonomy of a larger fraction of metagenomic contigs compared to other methods. Benchmarking of VirusTaxo on a collection of SARS-CoV-2 sequencing libraries and metavirome datasets suggests that VirusTaxo can characterize virus taxonomy from highly diverse contigs and provide a reliable decision on the taxonomy of viruses.

SENTIMENT ATTRIBUTION ANALYSIS WITH HIERARCHICAL CLASSIFICATION AND AUTOMATIC ASPECT CATEGORIZATION ON ONLINE USER REVIEWS

Due to COVID-19 pandemic, most physical business transactions were pushed online. Online reviews became an excellent source for sentiment analysis to determine a customer's sentiment about a business. This insight is valuable asset for businesses, especially for tourism sector, to be harnessed for business intelligence and craft new marketing strategies. However, traditional sentiment analysis with flat classification and manual aspect categorization technique imposes challenges with non-opinionated reviews and outdated pre-defined aspect categories which limits businesses to filter relevant opinionated reviews and learn new aspects from reviews itself for aspect-based sentiment analysis. Therefore, this paper proposes sentiment attribution analysis with hierarchical classification and automatic aspect categorization to improve the social listening for diligent marketing and recommend potential business optimization to revive the business from surviving to thriving after this pandemic. Hierarchical classification is proposed using hybrid approach. While automatic aspect categorization is constructed with semantic similarity clustering and applied enhanced topic modelling on opinionated reviews. Experimental results on two real-world datasets from two different industries, Airline and Hotel, shows that the sentiment analysis with hierarchical classification outperforms the classification accuracy with a good F1-score compared to baseline papers. Automatic aspect categorization was found to be able to unhide the sentiment of the aspects which was not recognized in manual aspect categorization. Although it is accepted that the effectiveness of aspect-based sentiment analysis on flat classification and manual aspect categorization, none have assessed the effectiveness while using hierarchical classification with a hybrid approach and automatic aspect categorization.

Characteristics of Clinical Trials Launched Early in the COVID-19 Pandemic in the US and in France.

Based on hierarchical classification and logistic regression of early US and French COVID-19 clinical trials we show that despite the registration of a large number of trials, only a minority had characteristics usually associated with providing robust and relevant evidence.