Abstract
With the Internet 2.0 era, managing user emotions is a problem that more and more actors are interested in. Historically, the first notions of emotion sharing were expressed and defined with emoticons. They allowed users to show their emotional status to others in an impersonal and emotionless digital world. Now, in the Internet of social media, every day users share lots of content with each other on Facebook, Twitter, Google+ and so on. Several new popular web sites like FlickR, Picassa, Pinterest, Instagram or DeviantArt are now specifically based on sharing image content as well as personal emotional status. This kind of information is economically very valuable as it can for instance help commercial companies sell more efficiently. In fact, with this king of emotional information, business can made where companies will better target their customers needs, and/or even sell them more products. Research has been and is still interested in the mining of emotional information from user data since then. In this paper, we focus on the impact of emotions from images that have been collected from search image engines. More specifically our proposition is the creation of a filtering layer applied on the results of such image search engines. Our peculiarity relies in the fact that it is the first attempt from our knowledge to filter image search engines results with an emotional filtering approach.
Highlights
Needs from users of the Web are evolving with new technologies
This paper proposes to contribute to these researches introducing a new layer to image search engines filters according them to add an image emotion status
We so presumed that its content do not imply hight emotional impact on viewers
Summary
Needs from users of the Web are evolving with new technologies. People are accustomed to participate and give their opinions on websites or social networks They share everyday emoticons, likes on Facebook, +1’s on Google+, short messages on Twitter. Web search engines users are often lost in the huge amount of answers they got from anonymous requests and want specific and personalized replies to their own needs. Image research engines often allow filtering their results. This is usually done by extraction and comparison of image features. Those can be deduced from bottom-up or top-down approaches [15].
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