Mining User-Aware Rare Sequential Topic Patterns in Document Streams

Mining User-Aware Rare Sequential Topic Patterns in Document Streams

 

Introduction

Document streams are created and distributed in various forms on the Internet, such as news streams, emails, micro-blog articles, chatting messages, research paper archives, web forum discussions, and so forth. The contents of these documents generally concentrate on some specific topics, which reflect offline social events and users’ characteristics in real life. 

To mine these pieces of information, many researches of text mining focused on extracting topics from document collections and document streams through various probabilistic topic models, such as classical PLSI, LDA and their extensions.

In order to characterize user behaviors in published document streams, we study on the correlations among topics extracted from these documents, especially the sequential relations, and specify them as Sequential Topic Patterns (STPs). Each of them records the complete and repeated behavior of a user when she is publishing a series of documents, and are suitable for inferring users’ intrinsic characteristics and psychological statuses. 

First, compared to individual topics, STPs capture both combinations and orders of topics, so can serve well as discriminative units of semantic association among documents in ambiguous situations. 

Second, compared to document-based patterns, topic-based patterns contain abstract information of document contents and are thus beneficial in clustering similar documents and finding some regularities about Internet users. 

Third, the probabilistic description of topics helps to maintain and accumulate the uncertainty degree of individual topics, and can thereby reach high confidence level in pattern matching for uncertain data. For a document stream, some STPs may occur frequently and thus reflect common behaviors of involved users. 

Beyond that, there may still exist some other patterns which are globally rare for the general population, but occur relatively often for some specific user or some specific group of users. We call them User-aware Rare STPs (URSTPs). Compared to frequent ones, discovering them is especially interesting and significant. Theoretically, it defines a new kind of patterns for rare event mining, which is able to characterize personalized and abnormal behaviors for special users. Practically, it can be applied in many real-life scenarios of user behavior analysis.

 Fig. 1. Mining User‐Aware Rare Sequential Topic Patterns in Document Streams

Mining URSTP

In this section, we propose a novel approach to mining URSTPs in document streams. The main processing framework for the task is shown in Fig. 2. It consists of three phases. At first, textual documents are crawled from some micro-blog sites or forums, and constitute a document stream as the input of our approach. Then, as preprocessing procedures, the original stream is transformed to a topic level document stream and then divided into many sessions to identify complete user behaviors. Finally and most importantly, we discover all the STP candidates in the document stream for all users, and further pick out significant URSTPs associated to specific users by user-aware rarity analysis.

Fig. 2. Processing framework of URSTP mining.