In the contemporary era, social media platforms amass an extensive volume of
social data contributed by their users. In order to promptly grasp the opinions
and emotional inclinations of individuals regarding a product or event, it
becomes imperative to perform sentiment analysis on the user-generated content.
Microblog comments often encompass both lengthy and concise text entries,
presenting a complex scenario. This complexity is particularly pronounced in
extensive textual content due to its rich content and intricate word
interrelations compared to shorter text entries. Sentiment analysis of public
opinion shared on social networking websites such as Facebook or Twitter has
evolved and found diverse applications. However, several challenges remain to
be tackled in this field. The hybrid methodologies have emerged as promising
models for mitigating sentiment analysis errors, particularly when dealing with
progressively intricate training data. In this article, to investigate the
hesitancy of COVID-19 vaccination, we propose eight different hybrid deep
learning models for sentiment classification with an aim of improving overall
accuracy of the model. The sentiment prediction is achieved using embedding,
deep learning model and grid search algorithm on Twitter COVID-19 dataset.
According to the study, public sentiment towards COVID-19 immunization appears
to be improving with time, as evidenced by the gradual decline in vaccine
reluctance. Through extensive evaluation, proposed model reported an increased
accuracy of 98.86%, outperforming other models. Specifically, the combination
of BERT, CNN and GS yield the highest accuracy, while the combination of GloVe,
BiLSTM, CNN and GS follows closely behind with an accuracy of 98.17%. In
addition, increase in accuracy in the range of 2.11% to 14.46% is reported by
the proposed model in comparisons with existing works.

通过使用嵌入、深度学习模型和网格搜索算法对 Twitter COVID-19 数据集进行情感分类，本研究提出了八种不同的混合深度学习模型，旨在提高模型的整体准确性，研究表明，COVID-19 疫苗接种的公众情绪随时间逐渐改善，所提出的模型在广泛的评估中报告了 98.86% 的增加准确性，超过其他模型。