Singh Mrityunjay, Jakhar Amit Kumar, Pandey Shivam
Department of CS and IT, Jaypee University of Information Technology, Waknaghat, 173234 India.
Department of CSE, Bundelkhand Institute of Engineering and Technology, Jhansi, 284128 India.
Soc Netw Anal Min. 2021;11(1):33. doi: 10.1007/s13278-021-00737-z. Epub 2021 Mar 19.
Nowadays, the whole world is confronting an infectious disease called the coronavirus. No country remained untouched during this pandemic situation. Due to no exact treatment available, the disease has become a matter of seriousness for both the government and the public. As social distance is considered the most effective way to stay away from this disease. Therefore, to address the people eagerness about the Corona pandemic and to express their views, the trend of people has moved very fast towards social media. Twitter has emerged as one of the most popular platforms among those social media platforms. By studying the same eagerness and opinions of people to understand their mental state, we have done sentiment analysis using the BERT model on tweets. In this paper, we perform a sentiment analysis on two data sets; one data set is collected by tweets made by people from all over the world, and the other data set contains the tweets made by people of India. We have validated the accuracy of the emotion classification from the GitHub repository. The experimental results show that the validation accuracy is 94%.
如今，全世界都在应对一种名为冠状病毒的传染病。在这种大流行情况下，没有一个国家能幸免。由于没有确切的治疗方法，这种疾病已成为政府和公众都极为重视的问题。由于社交距离被认为是远离这种疾病的最有效方法。因此，为了回应人们对新冠疫情的热切关注并表达他们的观点，人们迅速转向社交媒体。推特已成为这些社交媒体平台中最受欢迎的平台之一。通过研究人们同样的热切关注和观点来了解他们的心理状态，我们使用BERT模型对推文进行了情感分析。在本文中，我们对两个数据集进行情感分析；一个数据集是由来自世界各地的人发布的推文收集而来，另一个数据集包含印度人发布的推文。我们从GitHub仓库验证了情感分类的准确性。实验结果表明，验证准确率为94%。
