ParallelTopics: A probabilistic approach to exploring document collections

Scalable and effective analysis of large text corpora remains a chal-lenging problem as our ability to collect textual data continues to increase at an exponential rate. To help users make sense of large text corpora, we present a novel visual analytics system, Parallel-Topics, which integrates a state-of-the-art probabilistic topic model Latent Dirichlet Allocation (LDA) with interactive visualization. To describe a corpus of documents, ParallelTopics first extracts a set of semantically meaningful topics using LDA. Unlike most tra-ditional clustering techniques in which a document is assigned to a specific cluster, the LDA model accounts for different topical as-pects of each individual document. This permits effective full text analysis of larger documents that may contain multiple topics.To highlight this property of the model, ParallelTopics utilizes the par-allel coordinate metaphor to present the probabilistic distribution of a document across topics. Such representation allows the users to discover single-topic vs. multi-topic documents and the relative im-portance of each topic to a document of interest. In addition, sinc