Abstract
This thesis presents a model based on social group analysis to get a specific topic space, which can be applied to the general user’s posts helping to mine his interest distribution. The distribution can serve for personalized ads 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. First, for the text posts, do text segmentation, and remove stop words, and extract keywords. Similarly, run feature detection, clustering, and extract key visual words for image contents. Then filter those noisy and ambiguous posts. In order to get better performance of LDA, after aggregating the posts in one fan page into a text document and a photo document, respectively run the LDA (Latent Dirichlet Allocation) model. Each fan page through the LDA model will output a topic distribution of the text document and a topic distribution of the text part. Afterwards, find the highest value dimension of the distribution. Fan Pages sharing the same topic label vote for the dimensions with the highest values of their own distributions. The dimension getting the most votes can be assigned the topic label of these fan pages. Then check whether each dimension has a unique label. If not, it is necessary to adjust the LDA hyper parameters (the Dirichlet Parameter) and run the LDA again. So far, a topic space each dimension of which has a specific and meaningful topic label has been constructed. When the trained model is applied to the general user posts, we can get a personal interest distribution, the value of each dimension representing the user’s preference of certain topic. The experimental results show that the improved model can effectively mine user’s interests. 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; we propose a method to select the posts which can better explain the topic between texts and photos, taking advantage of multi-media data; the model can automatically choose the appropriate parameters; this method can be applied to the real data shared by users, whose result is comparable to the news data. Keywords: Topic model; interest mining; social media analysis.