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Ldagibbs: A Command for Topic Modeling in Stata Using Latent Dirichlet Allocation
The Stata Journal, 2018 This paper introduces the ldagibbs command which implements Latent Dirichlet Allocation in Stata. Latent Dirichlet Allocation is the most popular machine learning topic model. Topic models automatically cluster text documents into a user chosen number of
Carlo Schwarz
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Parallel Latent Dirichlet Allocation on GPUs
2018Latent Dirichlet Allocation (LDA) is a statistical technique for topic modeling. Since it is very computationally demanding, its parallelization has garnered considerable interest. In this paper, we systematically analyze the data access patterns for LDA and devise suitable algorithmic adaptations and parallelization strategies for GPUs. Experiments on
Gordon Euhyun Moon +5 more
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Latent dirichlet allocation for tag recommendation
Proceedings of the third ACM conference on Recommender systems, 2009Tagging systems have become major infrastructures on the Web. They allow users to create tags that annotate and categorize content and share them with other users, very helpful in particular for searching multimedia content. However, as tagging is not constrained by a controlled vocabulary and annotation guidelines, tags tend to be noisy and sparse ...
Ralf Krestel +2 more
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Enriched Latent Dirichlet Allocation for Sentiment Analysis
Expert Systems, 2020AbstractOne of the main benefits of unsupervised learning is that there is no need for labelled data. As a method of this category, latent Dirichlet allocation (LDA) estimates the semantic relations between the words of the text effectively and can play an important role in solving various issues, including emotional analysis in combination with other ...
Amjad Osmani +2 more
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Labeled Phrase Latent Dirichlet Allocation
2016In recent years, topic modeling, such as Latent Dirichlet Allocation (LDA) and its variations, has been widely used to discover the abstract topics in text corpora. There are two state-of-the-art topic models: Labeled LDA (LLDA) and PhraseLDA. LLDA is a supervised generative model which considers the label information, but it does not take into ...
Yi-Kun Tang, Xianling Mao, Heyan Huang
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Exploit latent Dirichlet allocation for collaborative filtering
Frontiers of Computer Science, 2018Previous work on the one-class collaborative filtering (OCCF) problem can be roughly categorized into pointwise methods, pairwise methods, and content-based methods. A fundamental assumption of these approaches is that all missing values in the user-item rating matrix are considered negative.
Zhoujun Li 0001 +5 more
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Bug localization using latent Dirichlet allocation
Information and Software Technology, 2010Context: Some recent static techniques for automatic bug localization have been built around modern information retrieval (IR) models such as latent semantic indexing (LSI). Latent Dirichlet allocation (LDA) is a generative statistical model that has significant advantages, in modularity and extensibility, over both LSI and probabilistic LSI (pLSI ...
Nicholas A Kraft, Letha H Etzkorn
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Latent dirichlet allocation in web spam filtering
Proceedings of the 4th international workshop on Adversarial information retrieval on the web, 2008Latent Dirichlet allocation (LDA) (Blei, Ng, Jordan 2003) is a fully generative statistical language model on the content and topics of a corpus of documents. In this paper we apply a modification of LDA, the novel multi-corpus LDA technique for web spam classification. We create a bag-of-words document for every Web site and run LDA both on the corpus
István Bíró +2 more
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Clustered Latent Dirichlet Allocation for Scientific Discovery
2019 IEEE International Conference on Big Data (Big Data), 2019Topic modeling, a method for extracting the underlying themes from a collection of documents, is an increasingly important component of the design of intelligent systems enabling the sense-making of highly dynamic and diverse streams of text data related but not limited to scientific discovery.
Christopher Gropp +4 more
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Latent Dirichlet allocation-based temporal summarization
International Journal of Web Information Systems, 2019PurposeDuring crises such as accidents or disasters, an enormous volume of information is generated on the Web. Both people and decision-makers often need to identify relevant and timely content that can help in understanding what happens and take right decisions, as soon it appears online.
Ahmed Amir Tazibt, Farida Aoughlis
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