Sentiment Analysis on COVID-19 Twitter Data Streams Using Deep Belief Neural Networks.
Comput Intell Neurosci
; 2022: 8898100, 2022.
Article
in English
| MEDLINE | ID: covidwho-1822115
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.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Social Media
/
COVID-19
Type of study:
Prognostic study
Limits:
Humans
Language:
English
Journal:
Comput Intell Neurosci
Journal subject:
Medical Informatics
/
Neurology
Year:
2022
Document Type:
Article
Affiliation country:
2022
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