Improvement of natural image search engines results by emotional ﬁltering
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.