Authors:
Mohamed Kharrat
;
Anis Jedidi
and
Faiez Gargouri
Affiliation:
University of Sfax, Tunisia
Keyword(s):
Twitter, Annotation, Image, Video.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Context Discovery
;
Data Analytics
;
Data Engineering
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Mining Multimedia Data
;
Mining Text and Semi-Structured Data
;
Pre-Processing and Post-Processing for Data Mining
;
Process Mining
;
Symbolic Systems
Abstract:
Nowadays, online social network “Twitter” represents a huge source of unrefined information in various
formats (text, video, photo), especially during events and abnormal cases/incidents. New features for
Twitter mobile application are now available, allowing user to publish direct photos online. This paper is
focusing on photos/videos taken by user and published in real time using only mobile devices. The aim is to
find candidates for annotation from Tweet stream, then to annotate them by taking into accounts several
features based only on tweets. A preprocessing step is necessary to exclude all useless tweets, we then
process textual content of the rest. As a final step, we consider an additional characterization (spatiotemporal
and saliency) to get outcome of the annotation as RDF triples.