| dc.creator |
KÜÇÜKSİLLE, Ecir Uğur |
|
| dc.creator |
Sevli, Onur |
|
| dc.date |
2017-03-31T21:00:00Z |
|
| dc.date.accessioned |
2020-10-06T10:48:05Z |
|
| dc.date.available |
2020-10-06T10:48:05Z |
|
| dc.identifier |
8c3a1eca-b374-4be3-a385-4e5e59bfd577 |
|
| dc.identifier |
10.17559/tv-20151020205558 |
|
| dc.identifier |
https://avesis.sdu.edu.tr/publication/details/8c3a1eca-b374-4be3-a385-4e5e59bfd577/oai |
|
| dc.identifier.uri |
http://acikerisim.sdu.edu.tr/xmlui/handle/123456789/65910 |
|
| dc.description |
Online environments and especially social networks have become a great alternative to advertisement publishing. In order to accomplish effective advertising it is important that the contents coincide with the expectations of the target audience. Considering that expectations may change over time, it is required to identify the orientation of the users in real time and dynamically. In this study, the messages shared by Turkish Twitter users were analysed in real time and the instant expectations of the users have been identified. To perform this work, a web service was designed which analyses the user's profile and presents the advertisements that suit best to expectations. A method called Heuristic Pruning Method (HPM) has been revealed in order to filter the most appropriate advertising content. The developed system has been tested on a voluntary participant group who actively uses Twitter, and the effectiveness of the system is demonstrated by the received feedback. |
|
| dc.language |
eng |
|
| dc.rights |
info:eu-repo/semantics/closedAccess |
|
| dc.title |
ADVERTISING RECOMMENDATION SYSTEM BASED ON DYNAMIC DATA ANALYSIS ON TURKISH SPEAKING TWITTER USERS |
|
| dc.type |
info:eu-repo/semantics/article |
|