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Slow mixing for Latent Dirichlet Allocation
Statistics & Probability Letters, 2017zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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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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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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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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Tweet Sentiment Analysis with Latent Dirichlet Allocation
International Journal of Information Retrieval Research, 2014The method proposed here analyzes the social sentiments from collected tweets that have at least 1 of 800 sentimental or emotional adjectives. By dealing with tweets posted in a half a day as an input document, the method uses Latent Dirichlet Allocation (LDA) to extract social sentiments, some of which coincide with our daily sentiments. The extracted
Masahiro Ohmura +2 more
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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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Proceedings of the 2014 ACM conference on Web science, 2014
Topic modeling, in particular the Latent Dirichlet Allocation (LDA) model, has recently emerged as an important tool for understanding large datasets, in particular, user-generated datasets in social studies of the Web. In this work, we investigate the instability of LDA inference, propose a new metric of similarity between topics and a criterion of ...
Sergei Koltsov +2 more
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Topic modeling, in particular the Latent Dirichlet Allocation (LDA) model, has recently emerged as an important tool for understanding large datasets, in particular, user-generated datasets in social studies of the Web. In this work, we investigate the instability of LDA inference, propose a new metric of similarity between topics and a criterion of ...
Sergei Koltsov +2 more
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Joint Latent Dirichlet Allocation for Social Tags
IEEE Transactions on Multimedia, 2018Social tags, serving as a textual source of simple but useful semantic metadata to reflect the user preference or describe the web objects, has been widely used in many applications. However, social tags have several unique characteristics, i.e., sparseness and data coupling (i.e., non-IIDness), which makes existing text analysis methods such as LDA ...
Jiangchao Yao +4 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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