Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review

The COVID-19 pandemic caused by the novel coronavirus SARS-CoV-2 occurred unexpectedly in China in December 2019. Tens of millions of confirmed cases and more than hundreds of thousands of confirmed deaths are reported worldwide according to the World Health Organisation. News about the virus is spreading all over social media websites. Consequently, these social media outlets are experiencing and presenting different views, opinions and emotions during various outbreak-related incidents. For computer scientists and researchers, big data are valuable assets for understanding people's sentiments regarding current events, especially those related to the pandemic. Therefore, analysing these sentiments will yield remarkable findings. To the best of our knowledge, previous related studies have focused on one kind of infectious disease. No previous study has examined multiple diseases via sentiment analysis. Accordingly, this research aimed to review and analyse articles about the occurrence of different types of infectious diseases, such as epidemics, pandemics, viruses or outbreaks, during the last 10 years, understand the application of sentiment analysis and obtain the most important literature findings. Articles on related topics were systematically searched in five major databases, namely, ScienceDirect, PubMed, Web of Science, IEEE Xplore and Scopus, from 1 January 2010 to 30 June 2020. These indices were considered sufficiently extensive and reliable to cover our scope of the literature. Articles were selected based on our inclusion and exclusion criteria for the systematic review, with a total of n = 28 articles selected. All these articles were formed into a coherent taxonomy to describe the corresponding current standpoints in the literature in accordance with four main categories: lexicon-based models, machine learning-based models, hybrid-based models and individuals. The obtained articles were categorised into motivations related to disease mitigation, data analysis and challenges faced by researchers with respect to data, social media platforms and community. Other aspects, such as the protocol being followed by the systematic review and demographic statistics of the literature distribution, were included in the review. Interesting patterns were observed in the literature, and the identified articles were grouped accordingly. This study emphasised the current standpoint and opportunities for research in this area and promoted additional efforts towards the understanding of this research field.

Increased Corneal Scarring after Phototherapeutic Keratectomy in Fuchs' Corneal Dystrophy

PURPOSE

A 63-year-old female with Fuchs' endothelial corneal dystrophy, stromal edema and subepithelial scarring was inappropriately treated with phototherapeutic keratectomy, leading to a central focal circular corneal scar and decreased visual acuity that required penetrating keratoplasty.

METHOD

The host corneal button was bisected and fixed in 10% formaldehyde and in glutaraldehyde immediately after its removal.

RESULTS

Light microscopy demonstrated a central area of absent Bowman's layer with a thin layer of subepithelial fibrosis, stromal corneal edema, and thickened Descemet's membrane.

CONCLUSION

Correct estimation of differential ablation rates of tissue and shallow, repeated ablations followed by slit-lamp microscopy and/or videokeratography help prevent over-treatment.