People have recently begun communicating their thoughts and viewpoints through user-generated multimedia material on social networking websites. This information can be images, text, videos, or audio. Recent years have seen a rise in the frequency of occurrence of this pattern. Twitter is one of the most extensively utilized social media sites, and it is also one of the finest locations to get a sense of how people feel about events that are linked to the Monkeypox sickness. This is because tweets on Twitter are shortened and often updated, both of which contribute to the platform's character. The fundamental objective of this study is to get a deeper comprehension of the diverse range of reactions people have in response to the presence of this condition. This study focuses on finding out what individuals think about monkeypox illnesses, which presents a hybrid technique based on CNN and LSTM. We have considered all three possible polarities of a user's tweet: positive, negative, and neutral. An architecture built on CNN and LSTM is utilized to determine how accurate the prediction models are. The recommended model's accuracy was 94% on the monkeypox tweet dataset. Other performance metrics such as accuracy, recall, and F1-score were utilized to test our models and results in the most time and resource-effective manner. The findings are then compared to more traditional approaches to machine learning. The findings of this research contribute to an increased awareness of the monkeypox infection in the general population.
翻译:最近,人们开始通过社交网络网站的用户生成多媒体材料来表达他们的想法和观点。这种信息可以是图像、文本、视频或音频。近年来,这种模式的发生频率有所上升。Twitter是使用最广泛的社交媒体网站之一,也是了解人们如何看待与天花病相关事件的最佳地点之一。这是因为Twitter上的推特缩短并经常更新,这都有助于平台的特性。本研究的基本目标是更深入地了解人们对这一状况的反应范围。这项研究的重点是找出个人对猴子天病的看法,这是基于CNN和LSTM的混合技术。我们考虑了用户推特的所有三种可能的极点:正面的、负面的和中性的。在CNN和LSTM上建立的一个架构被用来确定预测模型的准确性。建议模型在猴子天花推特数据集上达到94%的准确性。其他的性能指标,如准确性、回顾和F1-核心的研究结果,这是在CNNP和LSTM上呈现的混合技术。我们用户推介的三种可能的两极点:正面的、负面的和中性、负面的和中性研究发现,用来测定我们一般的模型和最有效果的研究结果的模型。用来用来测试的模型,用来测定的模型和最有效果的研究结果。