Sentiment Analysis CSAM Model to Discover Pertinent Conversations in Twitter Microblogs

• Published in: International journal of computer network and information security Vol. 14; no. 5; pp. 28 - 46
• Main Authors: Fadhli, Imen, Hlaoua, Lobna, Nazih Omri, Mohamed
• Format: Journal Article
• Language: English
• Published: Hong Kong Modern Education and Computer Science Press 01.10.2022
• Subjects: Conditional probability; Data mining; Datasets; Emotional icons; False information; Feasibility studies; Microblogs; Sentiment analysis; Social networks
• ISSN: 2074-9090; 2074-9104
• DOI: 10.5815/ijcnis.2022.05.03

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.