Sustainable Artificial Intelligence-Based Twitter Sentiment Analysis on COVID-19 Pandemic

被引:1
|
作者
Vaiyapuri, Thavavel [1 ]
Jagannathan, Sharath Kumar [2 ]
Ahmed, Mohammed Altaf [3 ]
Ramya, K. C. [4 ]
Joshi, Gyanendra Prasad [5 ]
Lee, Soojeong [5 ]
Lee, Gangseong [6 ]
机构
[1] Prince Sattam Bin Abdulaziz Univ, Coll Comp Engn & Sci, Al Kharj 11942, Saudi Arabia
[2] St Peters Univ, Frank J Guarini Sch Business, 2641 John F Kennedy Blvd, Jersey City, NJ 07306 USA
[3] Prince Sattam Bin Abdulaziz Univ, Coll Comp Engn & Sci, Dept Comp Engn, Al Kharj 11942, Saudi Arabia
[4] Sri Krishna Coll Engn & Technol, Dept EEE, Coimbatore 641008, India
[5] Sejong Univ, Dept Comp Sci & Engn, Seoul 05006, South Korea
[6] Kwangwoon Univ, Ingenium Coll, 20 Kwangwoon ro, Seoul 01897, South Korea
关键词
sustainability; sentiment analysis; low resource language; natural language processing; deep learning; pattern recognition; COVID-19; pandemic;
D O I
10.3390/su15086404
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
The COVID-19 outbreak is a disastrous event that has elevated many psychological problems such as lack of employment and depression given abrupt social changes. Simultaneously, psychologists and social scientists have drawn considerable attention towards understanding how people express their sentiments and emotions during the pandemic. With the rise in COVID-19 cases with strict lockdowns, people expressed their opinions publicly on social networking platforms. This provides a deeper knowledge of human psychology at the time of disastrous events. By applying user-produced content on social networking platforms such as Twitter, the sentiments and views of people are analyzed to assist in introducing awareness campaigns and health intervention policies. The modern evolution of artificial intelligence (AI) and natural language processing (NLP) mechanisms has revealed remarkable performance in sentimental analysis (SA). This study develops a new Marine Predator Optimization with Natural Language Processing for Twitter Sentiment Analysis (MPONLP-TSA) for the COVID-19 Pandemic. The presented MPONLP-TSA model is focused on the recognition of sentiments that exist in the Twitter data during the COVID-19 pandemic. The presented MPONLP-TSA technique undergoes data preprocessing to convert the data into a useful format. Furthermore, the BERT model is used to derive word vectors. To detect and classify sentiments, a bidirectional recurrent neural network (BiRNN) model is utilized. Finally, the MPO algorithm is exploited for optimal hyperparameter tuning process, and it assists in enhancing the overall classification performance. The experimental validation of the MPONLP-TSA approach can be tested by utilizing the COVID-19 tweets dataset from the Kaggle repository. A wide comparable study reported a better outcome of the MPONLP-TSA method over current approaches.
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页数:15
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