TY -的A2 Korobeinikov安德烈•AU -阿拉姆Kazi Nabiul AU -汗,Md Shakib盟——Dhruba阿布杜尔Rab盟——汗,穆罕默德Monirujjaman盟——Al-Amri圣战f . AU -马苏德•Mehedi盟——拉瓦希德荷Majdi PY - 2021 DA - 2021/12/02 TI -深上优于情绪分析COVID-19疫苗接种反应从Twitter数据SP—4321131六世- 2021 AB - COVID-19大流行对很多人产生了毁灭性的影响,产生严重的焦虑,恐惧,和复杂的情感或情绪。启动后对冠状病毒疫苗,人的感情变得更加多样化和复杂。我们的目标是了解和解决他们的情绪在本研究使用深度学习技巧。社交媒体是目前最好的方式来表达感情和情绪,和推特的帮助下,人们可以有个更好的主意是什么趋势和人们的思想。我们的动机研究是理解人的不同情绪对疫苗接种的过程。在这个研究中,收集的时间推July21从12月21日。包含的tweet信息最常见疫苗最近来自世界各地。各种情绪的人关于疫苗的使用自然语言处理(NLP)评估工具,价知道字典情绪Reasoner(维德)。初始化得到的极性情感分成三组(正面、负面和中性)帮助我们想象整个场景;-我们的发现包括积极的33.96%,17.55%,和48.49%中性反应。 In addition, we included our analysis of the timeline of the tweets in this research, as sentiments fluctuated over time. A recurrent neural network- (RNN-) oriented architecture, including long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM), was used to assess the performance of the predictive models, with LSTM achieving an accuracy of 90.59% and Bi-LSTM achieving 90.83%. Other performance metrics such as precision,, F1-score, and a confusion matrix were also used to validate our models and findings more effectively. This study improves understanding of the public’s opinion on COVID-19 vaccines and supports the aim of eradicating coronavirus from the world. SN - 1748-670X UR - https://doi.org/10.1155/2021/4321131 DO - 10.1155/2021/4321131 JF - Computational and Mathematical Methods in Medicine PB - Hindawi KW - ER -