Topic models are known to suffer from sparsity when applied to short text data. The problem is caused by a reduced number of observations available for a reliable inference (i.e.: the words in a document). A popular heuristic utilized to overcome this problem is to perform before training some form of document aggregation by context (e.g.: author, hashtag). We dedicated one part of this dissertation to modeling explicitly the implicit assumptions of the document aggregation heuristic and applying it to two well known model architectures: a mixture and an admixture. Our findings indicate that an admixture model benefits more from aggregation compared to a mixture model which rarely improved over its baseline (the standard mixture). We also find that the state of the art in short text data can be surpassed as long as every context is shared by a small number of documents. In the second part of the dissertation we develop a more general purpose topic model which can also be used when contextual information is not available. The proposed model is formulated around the observation that in normal text data, a classic topic model like an admixture works well because patterns of word co-occurrences arise across the documents. However, the possibility of such patterns to arise in a short text dataset is reduced. The model assumes every document is a bag of word co-occurrences, where each co-occurrence belongs to a latent topic. The documents are enhanced a priori with related co-occurrences from the other documents, such that the collection will have a greater chance of exhibiting word patterns. The proposed model performs well managing to surpass the state of the art and popular topic model baselines
