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Applying English Idiomatic Expressions to Classify Deep Sentiments in COVID-19 Tweets

Computer Systems Science & Engineering 2023年第10期47卷 37-54页

作者： bashar tahayna Ramesh Kumar Ayyasamy Faculty of Information and Communication Technology Universiti Tunku Abdul RahmanKampar31900Malaysia

Millions of people are connecting and exchanging information on social media platforms,where interpersonal interactions are constantly being ***,due to inaccurate or misleading information about the COVID-19 pandemic,social media platforms became the scene of tense debates between believers and *** professionals and public health agencies also use social media to inform the public about COVID-19 news and ***,they occasionally have trouble managing massive pandemic-related rumors and *** reason is that people share and engage,regardless of the information source,by assuming the content is unquestionably *** Twitter,users use words and phrases literally to convey their views or ***,other users choose to utilize idioms or proverbs that are implicit and indirect to make a stronger impression on the audience or perhaps to catch their *** and proverbs are figurative expressions with a thematically coherent totality that cannot understand *** more than 10%of tweets containing idioms or slang,most sentiment analysis research focuses on the accuracy enhancement of various classification ***,little attention would decipher the hidden sentiments of the expressed idioms in *** paper proposes a novel data expansion strategy for categorizing tweets concerning *** following are the benefits of the suggested method:1)no transformer fine-tuning is necessary,2)the technique solves the fundamental challenge of the manual data labeling process by automating the construction and annotation of the sentiment lexicon,3)the method minimizes the error rate in annotating the lexicon,and drastically improves the tweet sentiment classification’s accuracy performance.

关键词： Sentiment analysis idiomatic lexicon BERT COVID-19 deep learning

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