Mining Tags from Flickr User Comments Using a Hybrid Ranking Model

In the Web2.0 era, user generated content has become the main source of information of many popular websites such as Flickr. In Flickr, each user can share his/her photos and browse others´ easily. Tagging system is an important approach to the photo management in Flickr. Users can browse photos by clicking their attached tags. However, many photos have very few or even no tags, because only the up loader can mark tags for the photo. Meanwhile, when a user browses the photo he/she is interested in, he/she may have comments to express his/her independent viewpoint on the photo. Therefore, it is critical to recommend new tags or enrich the existing tag set based on user comments. Relying on Natural Language Processing (NLP) techniques, this paper introduces a word-based method in generating candidate tags extracted from user comments. In the phase of sorting and recommending tags, we propose an algorithm by jointly modeling the location information of candidate tags, statistical information and semantic similarity. Extensive experimental results demonstrate the effectiveness of our method.