A Social Media Ontology-Based Sentiment Analysis and Community Detection Framework: Brexit Case Study

Abstract

Nowadays social media content is the fuel of almost all kinds of domains, due to its rich and ever-increasing quantity of data. Digging this content can lead to extracting valuable information that can enhance business products and services, politic decisions, socio-economic systems and more. To this end, sentiment analysis and community detection represent two of the main methods used to analyze and comprehend human interactions within social media. Also, to get meaningful results, filtering social content is needed, here where domain ontology can be a great assistant in collecting specific data, as it describes the domain’s features and their existing relationships. This current work depicts our social media analysis Framework, where we apply lexicon-based and machine learning approaches to extract expressed sentiments of social media users toward a subject, and also used a community detection algorithm to highlight the formed groups within the network. Besides, the resulting Framework not only focuses on analyzing textual data (by taking into account the negation and sentence POS tags), but also visual content shared by users, such as images. For the testing purpose of our Framework, we chose to analyze the British exit (“Brexit”) case by collecting ontology-based data from Twitter and Reddit, and it had some promising results.