A hierarchical topic modelling approach for tweet clustering

While social media platforms such as Twitter can provide rich and up-to-date information for a wide range of applications, manually digesting such large volumes of data is difficult and costly. Therefore it is important to automatically infer coherent and discriminative topics from tweets. Conventional topic models and document clustering approaches fail to achieve good results due to the noisy and sparse nature of tweets. In this paper, we explore various ways of tackling this challenge and finally propose a two-stage hierarchical topic modelling system that is efficient and effective in alleviating the data sparsity problem. We present an extensive evaluation on two datasets, and report our proposed system achieving the best performance in both document clustering performance and topic coherence

A Gamma-Poisson Mixture Topic Model for Short Text

Most topic models are constructed under the assumption that documents follow a multinomial distribution. The Poisson distribution is an alternative distribution to describe the probability of count data. For topic modelling, the Poisson distribution describes the number of occurrences of a word in documents of fixed length. The Poisson distribution has been successfully applied in text classification, but its application to topic modelling is not well documented, specifically in the context of a generative probabilistic model. Furthermore, the few Poisson topic models in literature are admixture models, making the assumption that a document is generated from a mixture of topics. In this study, we focus on short text. Many studies have shown that the simpler assumption of a mixture model fits short text better. With mixture models, as opposed to admixture models, the generative assumption is that a document is generated from a single topic. One topic model, which makes this one-topic-per-document assumption, is the Dirichlet-multinomial mixture model. The main contributions of this work are a new Gamma-Poisson mixture model, as well as a collapsed Gibbs sampler for the model. The benefit of the collapsed Gibbs sampler derivation is that the model is able to automatically select the number of topics contained in the corpus. The results show that the Gamma-Poisson mixture model performs better than the Dirichlet-multinomial mixture model at selecting the number of topics in labelled corpora. Furthermore, the Gamma-Poisson mixture produces better topic coherence scores than the Dirichlet-multinomial mixture model, thus making it a viable option for the challenging task of topic modelling of short text.Comment: 26 pages, 14 Figures, to be published in Mathematical Problems in Engineerin

Search Result Diversification in Short Text Streams

We consider the problem of search result diversification for streams of short texts. Diversifying search results in short text streams is more challenging than in the case of long documents, as it is difficult to capture the latent topics of short documents. To capture the changes of topics and the probabilities of documents for a given query at a specific time in a short text stream, we propose a dynamic Dirichlet multinomial mixture topic model, called D2M3, as well as a Gibbs sampling algorithm for the inference. We also propose a streaming diversification algorithm, SDA, that integrates the information captured by D2M3 with our proposed modified version of the PM-2 (Proportionality-based diversification Method -- second version) diversification algorithm. We conduct experiments on a Twitter dataset and find that SDA statistically significantly outperforms state-of-the-art non-streaming retrieval methods, plain streaming retrieval methods, as well as streaming diversification methods that use other dynamic topic models