LAUSR

This paper presents a supervised learning method for paranoid detection in French tweets. A classifier uses four groups of features (textual, linguistic, meta-data, timeline) that exploit a hybrid approach. This approach uses information obtained from the text of tweets by applying Natural Language Processing (NLP) techniques to analyse them, such as morphological analysis, syntactic analysis and sentence embedding. Thus, information about the user such as the number of followers and the number of shared posts. Besides, information about tweets such as the number of symbols and the number of hashtags. Moreover, information about the publication date of tweets such as the number of postings in the morning. Finally, statistical techniques to combine and filter the different types of features extracted from the previous steps in order to calculate the distance between the training corpus (the labelled data) and the test corpus (unlabelled data). In addition, the state mentioned statistical techniques are used for detecting the writing style of patients. In general, our method benefits from different types of features and preserves the principle of relativity by taking advantage of fuzzy logic. Our results are encouraging with an accuracy of 78% for the detection of paranoid people and 70% for the detection of the behaviour of these people towards Covid-19.