Authors:
David Nettleton
1
;
Mari-Carmen Marcos
1
and
Bartolomé Mesa-Lao
2
Affiliations:
1
Pompeu Fabra University, Spain
;
2
Autonomous University of Barcelona, Spain
Keyword(s):
Image tagging, Tag recommendation, User support, Statistical analysis, Data modeling.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Data Analytics
;
Data Engineering
;
Information Extraction
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Structured Data Analysis and Statistical Methods
;
Symbolic Systems
Abstract:
Image tagging in Internet is becoming a crucial aspect in the search activity of many users all over the world, as online content evolves from being mainly text based, to being multi-media based (text, images, sound, …). In this paper we present a study carried out for native and non native English language taggers, with the objective of providing user support depending on the detected language skills and characteristics of the user. In order to do this, we analyze the differences between how users tag objectively (using what we call ‘see’ type tags) and subjectively (by what we call ‘evoke’ type tags). We study the data using bivariate correlation, visual inspection and rule induction. We find that the objective/subjective factors are discriminative for native/non native users and can be used to create a data model. This information can be utilized to help and support the user during the tagging process.