Abstract
This paper is High-Level Attributes Based User Interests Mining from Social Media. The use of the user community pages shared by graphic content. This thesis presents a method jointly using textual and High-Level Attributes feature of user generated social media data to mine user interest distribution, which can be applied to the general user’s posts helping to mine his interest distribution. The mined distribution can serve for personalized ads recommendation or social content recommendation. The framework consists of three steps: the preprocessing and feature extraction step, the Labeled Topic Space learning step, and the user interests mining step. The study chooses the Facebook fan pages which have topic labels as the training data. We were selected 10 topic, each 20 fanpages. First, for the text posts, do text segmentation, and remove stop words, and extract keywords. After capturing image content by using densecap, get some caption, and to do text segmentation, and remove stop words, and extract keywords. And then we can get image's high-level attributes. Use LDA to check whether each dimension has a unique label. Finally, applied to the general user community site data, as long as the user is sharing image into the model training good theme for processing, you can get the user's interest profile, wherein the representative value of each dimension user preferences for a certain degree of interest. Analysis of interest to the user through this distribution method. The experimental results shows the improved effectiveness of the proposed method. Also, the image recommendation demonstration verifies the feasibility of our method applied on real data. The main contribution of this study contains four parts: it can solve the problem that the conventional unsupervised LDA can’t reveal the specific meaning of each dimension of the topic space; Use denscap capture high-level attributes out of the image, to make the image more reliable analytical results; Combining text and image results, and learn from each other and make full use of multimedia advantage. Keywords: Topic model; interest mining; social media analysis; high-level attributes;