PP234 Analysis Of Discussions On Twitter On The Topic Of COVID-19 Tests: Exploring A Natural Language Processing Approach

IntroductionVarious strategies to suppress the Coronavirus have been adopted by governments across the world; one such strategy is diagnostic testing. The anxiety of testing on individuals is difficult to quantify. This analysis explores the use of soft intelligence from Twitter (USA, UK & India) in helping better understand this issue.MethodsA total of 650,000 tweets were collected between September and October 2020, using Twitter API using hashtags such as ‘#oxymeter’, ‘#oximeter’, ‘#antibodytest’, ‘#infraredthermometer’, ‘#swabtest’, ‘#rapidtest’, and ‘#antigen’. We applied natural language processing (TextBlob) to assign sentiment and categorize the tweets by emotions and attitude. WordCloud was then used to identify the single topmost 500 words in the whole tweet dataset.ResultsGlobal analysis and pre-processing of the tweets indicate that 21 percent, seven percent and four percent of tweets originated from the USA, UK, and India respectively. The tweets from #antibody, #rapid, #antigen, and #swabtest were positive sentiments, whereas #oxymeter, #infraredthermometer were mostly neutral. The underlying emotions of the tweets were approximately 2.5 times more positive than negative. The most used words in the tweets included ‘hope’ ‘insurance’, ‘symptoms’, ‘love’, ‘painful’, ‘cough’, ‘fast test’, ‘wife’, and ‘kids’.ConclusionsThe finding suggests that it may be reasonable to infer that people are generally concerned about their personal and social wellbeing, wanting to keep themselves safe and perceive testing to deliver some component of that feeling of safety. There are several limitations to this study such as it was restricted to only three countries, and includes only English language tweets with a limited number of hashtags.

Crack Growth Signal Processing Approach Combining Wavelet Threshold Denoising and Variable Amplitude DCPD Technique

The direct current potential drop (DCPD) method is widely used in laboratory environments to monitor the crack initiation and propagation of specimens. In this study, an anti-interference signal processing approach, combining wavelet threshold denoising and a variable current amplitude DCPD signal synthesis technique, was proposed. Adaptive wavelet threshold denoising using Stein’s unbiased risk estimate was applied to the main potential drop signal and the reference potential signal under two different current amplitudes to reduce the interference caused by noise. Thereafter, noise-reduced signals were synthesized to eliminate the time-varying thermal electromotive force. The multiplicative interference signal was eliminated by normalizing the main potential drop signal and the reference potential drop signal. This signal processing approach was applied to the crack growth monitoring data of 316 L stainless steel compact tension specimens in a laboratory environment, and the signal processing results of static cracks and propagation cracks under different load conditions were analyzed. The results showed that the proposed approach can significantly improve the signal-to-noise ratio as well as the accuracy and resolution of the crack growth measurement.