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Probabilistic Topic Models [PDF]
IEEE Signal Processing Magazine, 2010 In this article, we review probabilistic topic models: graphical models that can be used to summarize a large collection of documents with a smaller number of distributions over words. Those distributions are called "topics" because, when fit to data, they capture the salient themes that run through the collection.
David M. Blei +2 more
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Enhancing Indonesian customer complaint analysis: LDA topic modelling with BERT embeddings
Jurnal Ilmiah SINERGI, 2023 Social media data can be mining for recommended systems to know the best trends or patterns. The customers have the freedom to ask questions about the product, tell their demands, and convey their complaints through social media.
Mutiara Auliya Khadija +1 more
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CoRR, 2011 Latent Dirichlet Allocation models discrete data as a mixture of discrete distributions, using Dirichlet beliefs over the mixture weights. We study a variation of this concept, in which the documents' mixture weight beliefs are replaced with squashed Gaussian distributions.
Hennig, P. +3 more
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A Topic Coverage Approach to Evaluation of Topic Models [PDF]
IEEE Access, 2021 Topic models are widely used unsupervised models capable of learning topics - weighted lists of words and documents - from large collections of text documents. When topic models are used for discovery of topics in text collections, a question that arises naturally is how well the model-induced topics correspond to topics of interest to the analyst.
Damir Korencic +3 more
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Uncovering dynamic textual topics that explain crime
Royal Society Open Science, 2021 Crime analysis/mapping techniques have been developed and applied for crime detection and prevention to predict where and when crime occurs, leveraging historical crime records over a spatial area and covariates for the spatial domain.
Seppo Virtanen
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CoRR, 2010 The syntactic topic model (STM) is a Bayesian nonparametric model of language that discovers latent distributions of words (topics) that are both semantically and syntactically coherent. The STM models dependency parsed corpora where sentences are grouped into documents.
Jordan L. Boyd-Graber, David M. Blei
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Polylingual topic models [PDF]
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing Volume 2 - EMNLP '09, 2009 Topic models are a useful tool for analyzing large text collections, but have previously been applied in only monolingual, or at most bilingual, contexts. Meanwhile, massive collections of interlinked documents in dozens of languages, such as Wikipedia, are now widely available, calling for tools that can characterize content in many languages.
Mimno, David +4 more
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Land, 2023 As digitalization increasingly gains attention in the agriculture sector, many African youths are seizing the opportunity provided by digital technologies to engage in agriculture.
Matthew Ayamga +3 more
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Sensors, 2023 Recommender systems are becoming an integral part of routine life, as they are extensively used in daily decision-making processes such as online shopping for products or services, job references, matchmaking for marriage purposes, and many others ...
Balraj Kumar +4 more
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