Sentiment Analysis on COVID-19 Twitter Data Streams Using Deep Belief Neural Networks

Abstract

Social media is Internet-based by design, allowing people to share content quickly via electronic means. People can openly express their thoughts on social media sites such as Twitter, which can then be shared with other people. During the recent COVID-19 outbreak, public opinion analytics provided useful information for determining the best public health response. At the same time, the dissemination of misinformation, aided by social media and other digital platforms, has proven to be a greater threat to global public health than the virus itself, as the COVID-19 pandemic has shown. The public's feelings on social distancing can be discovered by analysing articulated messages from Twitter. The automated method of recognizing and classifying subjective information in text data is known as sentiment analysis. In this research work, we have proposed to use a combination of preprocessing approaches such as tokenization, filtering, stemming, and building N-gram models. Deep belief neural network (DBN) with pseudo labelling is used to classify the tweets. Top layers of the base classifiers are boosted in the pseudo labelling strategy, whereas lower levels of the base classifiers share weights for feature extraction. By introducing the pseudo boost mechanism, our suggested technique preserves the same time complexity as a DBN while achieving fast convergence to optimality. The pseudo labelling improves the performance of the classification. It extracts the keywords from the tweets with high precision. The results reveal that using the DBN classifier in conjunction with the bigram in the N-gram model outperformed other models by 90.3 percent. The proposed approach can also aid medical professionals and decision-makers in determining the best course of action for each location based on their views regarding the pandemic.

What do people write about COVID-19 and teaching, publicly? Insulators and threats to newly habituated and institutionalized practices for instruction

“…Each of these tweets show a mixed response of negative and positive text that registered on the lexicon. This dual emotion of fear mixed with positive feelings was documented in the literature, where it was noted that fear was represented in twitter covid corpora that simultaneously in some cases also held, although these latter documentations were more about vaccine availability in the UK and more general sentiments, respectively [ 57 , 58 , 62 ].…”

“…Some twitter articles report on the overall model tunings and findings on large machine learning models. These have showcased the power of big data analytics [57][58][59][60][61]. Still other articles report on the health findings about masking, vaccines, and overall health discourse [62][63][64].…”

Speech emotion recognition with artificial intelligence for contact tracing in the COVID‐19 pandemic

“…Moreover in Ref. [45], the authors used Twitter‐based analysis for understanding people's feelings on social distancing from Twitter's data.…”

Social mood during the Covid-19 vaccination process in Spain. A sentiment analysis of tweets and social network leaders

“…These two reasons make Twitter the favorite social network when carrying out studies of this type [ [34] , [35] , [36] , [37] , [38] ]. In particular, there are numerous studies on the effects of COVID-19 on society [ 28 , [39] , [40] , [41] , [42] ].…”