Needle in a haystack: an empirical study on mining tags from Flickr user comments Online publication date: Thu, 23-May-2019
by Haijun Zhang; Jingxuan Li; Bin Luo; Yan Li
International Journal of Information Technology and Management (IJITM), Vol. 18, No. 2/3, 2019
Abstract: In the Web2.0 era, user generated content has become the main source of information of many popular photo-sharing websites such as Flickr. In Flickr, many photos have very few or even no tags, because only the uploader can mark tags for a photo. Meanwhile, the user can deliver his/her comment on the photo, which he/she is browsing. Therefore, it is possible to recommend new tags or enrich the existing tag set based on user comments. The work of this paper contains two phases, i.e., the tag generation, and the ranking algorithm. In the phase of candidate tags generation, two methods are introduced relying on natural language processing (NLP) techniques, namely word-based and phrase-based. In ranking and recommending tags, we proposed an algorithm by jointly modelling the location information of candidate tags, statistical information of candidate tags and semantic similarity between candidate tags. Extensive experimental results demonstrate the effectiveness of our method.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Information Technology and Management (IJITM):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook