Clustering and topic modeling over tweets: A comparison over a health dataset Twitter became the most popular form of social interactions in the healthcare domain. Thus, various teams have evaluated Twitter as an additional source where patients share information about their healthcare with the potential goal to improve their outcomes. Several existing topic modeling and document clustering applications have been adapted to assess tweets showing that the performances of the applications are negatively affected due to the nature and characteristics of tweets. Moreover, Twitter health research has become difficult to measure because of the absence of comparisons between the existing applications. In this paper, we perform an evaluation based on internal indexes of different topic modeling and document clustering applications over two Twitter healthrelated datasets. Our results show that Online Twitter LDA and Gibbs LDA get a better performance for extracting topics and grouping tweets. We want to provide health practitioners this comparison to select the most suitable application for their tasks.

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.