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Aspect-based opinion mining is widely applied to review data to aggregate or summarize opinions of a product, and the current state-of-the-art is achieved with Latent Dirichlet Allocation (LDA)-based model. Although social media data like tweets are laden with opinions, their "dirty" nature (as natural language) has discouraged researchers from applying(More)
It has been previously noted that optimization of the <i>n</i>-call@<i>k</i> relevance objective (i.e., a set-based objective that is 1 if at least <i>n</i> documents in a set of <i>k</i> are relevant, otherwise 0) encourages more result set diversification for smaller <i>n</i>, but this statement has never been formally quantified. In this work, we(More)
Twitter data is extremely noisy – each tweet is short, unstructured and with informal language, a challenge for current topic modeling. On the other hand, tweets are accompanied by extra information such as authorship, hashtags and the user-follower network. Exploiting this additional information, we propose the Twitter-Network (TN) topic model to jointly(More)
This supplementary material looks at performing Bayesian inference on the Twitter-Network Topic Model (TNTM) presented in the main article. In the TNTM, combining a GP with a HPYP makes its posterior inference non-trivial. Hence, we employ approximate inference by alternatively performing MCMC sampling on the HPYP topic model and the network model,(More)
Bibliographic analysis considers author’s research areas, the citation network and paper content among other things. In this paper, we combine these three in a topic model that produces a bibliographic model of authors, topics and documents using a non-parametric extension of a combination of the Poisson mixed-topic link model and the author-topic model. We(More)
Bibliographic analysis considers the author’s research areas, the citation network and the paper content among other things. In this paper, we combine these three in a topic model that produces a bibliographic model of authors, topics and documents, using a non-parametric extension of a combination of the Poisson mixed-topic link model and the author-topic(More)
We propose a simulation method for multidimensional Hawkes processes based on superposition theory of point processes. This formulation allows us to design efficient simulations for Hawkes processes with differing exponentially decaying intensities. We demonstrate that inter-arrival times can be decomposed into simpler auxiliary variables that can be(More)
This note studies the bias arises from the MLE estimate of the rate parameter and the mean parameter of an exponential distribution. 1 Motivation Although maximum likelihood estimation (MLE) methods provide estimates that are useful, the estimates themselves are not guaranteed to be unbiased. Nevertheless, MLE methods are still highly regarded in practice(More)
Appendix A. On Modelling the Document-topic Hierarchy Here we discuss the motivation of modelling the document-topic hierarchy (θ and θ′) in more details. As mentioned in the paper, such modelling allows the citation information to be given more strength compared to the text information. Recall that each citation and each word correspond to a customer count(More)