Imen Fadhli

Work place: MARS Research Laboratory LR17ES05, University of Sousse, ISITCom, 4011 Hammam Sousse, Tunisia



Research Interests:


Imen Fadhli is currently a Ph.D. student at the Higher Institute of Computer Science and Telecom (ISITCom), University of Sousse, Tunisia. She received her Masters degree from the Higher Institute of Management, University of Sousse, Tunisia; in computer science in 2017. Her current research interests include the detection of the credible conversation in Twitter microblogs. She is member of the research Laboratory MARS (Modeling of Automated Reasoning Systems).

Author Articles
Sentiment Analysis CSAM Model to Discover Pertinent Conversations in Twitter Microblogs

By Imen Fadhli Lobna Hlaoua Mohamed Nazih Omri

DOI:, Pub. Date: 8 Oct. 2022

In recent years, the most exploited sources of information such as Facebook, Instagram, LinkedIn and Twitter have been considered to be the main sources of misinformation. The presence of false information in these social networks has a very negative impact on the opinions and the way of thinking of Internet users. To solve this problem of misinformation, several techniques have been used and the most popular is the sentiment analysis. This technique, which consists in exploring opinions on corpora of texts, has become an essential topic in this field. In this article, we propose a new approach, called Conversational Sentiment Analysis Model (CSAM), allowing, from a text written on a subject through messages exchanged between different users, called a conversation, to find the passages describing feelings, emotions, opinions and attitudes. This approach is based on: (i) the conditional probability in order to analyse sentiments of different conversation items in Twitter microblog, which are characterized by small sizes, the presence of emoticons and emojis, (ii) the aggregation of conversation items using the uncertainty theory to evaluate the general sentiment of conversation. We conducted a series of experiments based on the standard Semeval2019 datasets, using three standard and different packages, namely a library for sentiment analysis TextBlob, a dictionary, a sentiment reasoner Flair and an integration-based framework for the Vader NLP task. We evaluated our model with two dataset SemEval 2019 and ScenarioSA, the analysis of the results, which we obtained at the end of this experimental study, confirms the feasibility of our model as well as its performance in terms of precision, recall and F-measurement.

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