Madani Youness, Erritali Mohammed, Bouikhalene Belaid
Sultan Moulay Slimane University, Beni Mellal, Morocco.
Multimed Tools Appl. 2023 Feb 22:1-20. doi: 10.1007/s11042-023-14514-x.
Since the beginning of the covid-19 crisis, people from all over the world have used social media platforms to publish their opinions, sentiments, and ideas about the coronavirus epidemic and their news. Due to the nature of social networks, users share an immense amount of data every day in a freeway, which gives them the possibility to express opinions and sentiments about the coronavirus pandemic regardless of the time and the place. Moreover, The rapid number of exponential cases globally has become the apprehension of panic, fear, and anxiety among people. In this paper, we propose a new sentiment analysis approach to detect sentiments in Moroccan tweets related to covid-19 from March to October 2020. The proposed model is a recommender approach using the advantages of recommendation systems for classifying each tweet into three classes: positive, negative, or neutral. Experimental results show that our method gives good accuracy(86%) and outperforms the well-known machine learning algorithms. We find also that the sentiments of users changed from period to period, and that the evolution of the epidemiological situation in morocco affects the sentiments of users.
自新冠疫情危机开始以来，世界各地的人们利用社交媒体平台发表他们对新冠病毒疫情的看法、情绪和观点以及相关新闻。由于社交网络的特性，用户每天在信息高速公路上分享大量数据，这使他们无论何时何地都能表达对新冠疫情的看法和情绪。此外，全球指数级增长的病例数引发了人们的恐慌、恐惧和焦虑。在本文中，我们提出一种新的情感分析方法，用于检测2020年3月至10月摩洛哥与新冠疫情相关推文的情感。所提出的模型是一种推荐方法，利用推荐系统的优势将每条推文分为三类：积极、消极或中性。实验结果表明，我们的方法具有良好的准确率（86%），并且优于著名的机器学习算法。我们还发现，用户的情绪随时间变化，摩洛哥流行病学情况的演变影响着用户的情绪。
