Needle in a haystack: an empirical study on mining tags from Flickr user comments. (10th May 2019)

• Record Type: Journal Article
• Authors: Zhang, Haijun; Li, Jingxuan; Luo, Bin; Li, Yan
• Is Part Of: International journal of information technology and management. Volume 18:Number 2/3(2019)
• Page Start: 297
• Page End: 326
• Publication Date: 2019-05-10
• Subjects: tag recommendation -- user comment -- Flickr -- image annotation; Management information systems -- Periodicals; Information technology -- Periodicals; Management -- Data processing -- Periodicals; 658.403805
• Languages: English
• ISSNs: 1461-4111

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.