A Meaningful Information Extraction System for Interactive Analysis of Documents This paper is related to a project aiming at discovering weak signals from different streams of information, possibly sent by whistleblowers. The study presented in this paper tackles the particular problem of clustering topics at multilevels from multiple documents, and then extracting meaningful descriptors, such as weighted lists of words for document representations in a multidimensions space. In this context, we present a novel idea which combines Latent Dirichlet Allocation and Word2vec (providing a consistency metric regarding the partitioned topics) as potential method for limiting the a priori number of cluster K usually needed in classical partitioning approaches. We proposed 2 implementations of this idea, respectively able to: (1) finding the best K for LDA in terms of topic consistency; (2) gathering the optimal clusters from different levels of clustering. We also proposed a nontraditional visualization approach based on a multiagents system which combines both dimension reduction and interactivity.

Document Clustering and Topic Modeling: A Unified Bayesian Probabilistic Perspective Document clustering and topic modeling are fundamental tasks in text mining, that can be unified to reciprocally enhance each other. In this paper, we present a machine learning approach to the joint modeling and interdependent fulfilment of both tasks. In particular, document clustering and topic modeling are seamlessly interrelated under an innovative Bayesian generative model of clusters, topics and contents in text corpora. Such a model assumes that text corpora result from a generative process, in which clusters and topics act as connected latent factors. Essentially, clusters are initially associated with descriptive and actionable topic distributions, that enforce cluster coherence. The individual documents are then assigned to one respective cluster and worded accordingly. Under the devised model, document clustering and topic modeling can be simultaneously performed in an interdependent manner simply by Bayesian reasoning. For this purpose, the mathematical details regarding collapsed Gibbs sampling as well as parameter estimation are derived and implemented into an approximate inference algorithm. Comparative experiments on standard benchmark text corpora reveal the effectiveness of our approach at jointly clustering text documents and unveiling their semantics in terms of coherent topics.