Sentimental event detection from Arabic tweets

Abstract

This article presents and evaluates an approach to detect sentimental events from Twitter Arabic data streams. Sentimental events attract strongly opinionated responses from the online community; therefore, we aim at detecting the association of a topic with a positive or a negative sentiment at a particular time. To achieve that, we build sentimental time series where the frequencies of that association (between topics and sentiment) are recorded. And then, we use several algorithms to locate possible events. Events in positive timelines will be considered as positive, and similarly for negative events. Our approaches use Shannon diversity index and hill climbing peak finding. We experimented our proposed algorithms with the domain of football (soccer) news. The results showed good precision and recall considering mainstream media as a reference. The success of such experiment can open the door for many useful applications including reputation and brand monitoring systems for various domains and languages.