A new sentiment analysis method to detect and Analyse sentiments of Covid-19 moroccan tweets using a recommender approach.

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.

Using artificial intelligence techniques for detecting Covid-19 epidemic fake news in Moroccan tweets

During the covid-19 pandemic, a considerable amount of data travels fast worldwide on the net, mainly on the social media platform where people all over the world have constant and easy access to submit materials and posts. A considerable amount of shared news embeds misleading information which affects negatively the cognitive and psychological health of its readers. The present case study focuses on fake news being tweeted during the coronavirus pandemic for the purpose to mislead the targeted population. In this context, this paper exhibits a new approach to detect fake news on Twitter during the Covid-19 period. The proposed method consists of a classification approach that uses new tweets’ features and it is based on natural language processing, machine learning, and deep learning. The method is implemented in parallel with apache spark. Experimental results show that our approach yields very valuable results once it is used with the random forest algorithm with an accuracy equal to 79%. We also demonstrate that the sentiment of tweets plays an important role in the detection of fake news. Indeed, the model we present outperforms those models lacking consideration of new tweets’ features.