Abstract：To explore the distribution and correlation from massive Twitter data helps the accurate personalized recommendation. On-Line Analytical Processing (OLAP) provides an intuitive form that is suitable for people to explore the Twitter data. The key of applying OLAP to Twitter data is how to mine and build dimension hierarchy of tweeter interests. Different from the existing approaches that can extract interests of tweeters with only one level, an approach to the extraction of dimension hierarchy of interests for OLAP is proposed. Firstly, it retrieves the Twitter data through RestAPI. Afterwards, it detects the interests and sub-interests using an improved (Latent Dirichlet Allocation, LDA) model. Based on the extracted interests and sub-interests it finally constructs the dimension hierarchy of interests. The experiment verifies its effectiveness and scalability, and demonstrates it can extract dimension hierarchy of tweeters’ interests for OLAP more effectively than LDA and hLDA.
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