论文标题
利用Distilbert Transformer模型进行Covid-19的波斯开放文本响应的情感分类
Utilizing distilBert transformer model for sentiment classification of COVID-19's Persian open-text responses
论文作者
论文摘要
199年大流行在各个方面都引起了人类生活的急剧替代。在这方面，政府的法律影响了所有人的生活方式。由于这一事实，研究个人的情感对于了解即将到来的大流行的未来影响至关重要。为了促进这一目标，我们提出了一个NLP（自然语言处理）模型，以在波斯语中的一项调查中分析开放文本答案，并检测伊朗人民的正面和负面感觉。在这项研究中，使用Distilbert Transformer模型来执行此任务。我们采用了三种方法来进行比较，我们的最佳模型可以提高准确性：0.824，精度：0.824，召回：0.798和F1分数：0.804。
The COVID-19 pandemic has caused drastic alternations in human life in all aspects. The government's laws in this regard affected the lifestyle of all people. Due to this fact studying the sentiment of individuals is essential to be aware of the future impacts of the coming pandemics. To contribute to this aim, we proposed an NLP (Natural Language Processing) model to analyze open-text answers in a survey in Persian and detect positive and negative feelings of the people in Iran. In this study, a distilBert transformer model was applied to take on this task. We deployed three approaches to perform the comparison, and our best model could gain accuracy: 0.824, Precision: 0.824, Recall: 0.798, and F1 score: 0.804.
