Latent dirichlet allocation

doi:10.5555/944919.944937

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Latent dirichlet allocation

Journal Article•DOI•

Latent dirichlet allocation

David M. Blei¹, Andrew Y. Ng², Michael I. Jordan¹•Institutions (2)

University of California, Berkeley¹, Stanford University²

01 Mar 2003-Journal of Machine Learning Research (JMLR.org)-Vol. 3, pp 993-1022

TL;DR: This work proposes a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams, and Hofmann's aspect model.

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Abstract: We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. Each topic is, in turn, modeled as an infinite mixture over an underlying set of topic probabilities. In the context of text modeling, the topic probabilities provide an explicit representation of a document. We present efficient approximate inference techniques based on variational methods and an EM algorithm for empirical Bayes parameter estimation. We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model.

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Proceedings Article•DOI•

Mining topics in documents: standing on the shoulders of big data

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Zhiyuan Chen¹, Bing Liu¹•Institutions (1)

University of Illinois at Chicago¹

24 Aug 2014

TL;DR: This research proposes to learn as humans do, i.e., retaining the results learned in the past and using them to help future learning, and mines two forms of knowledge: must-link and cannot-link.

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Abstract: Topic modeling has been widely used to mine topics from documents. However, a key weakness of topic modeling is that it needs a large amount of data (e.g., thousands of documents) to provide reliable statistics to generate coherent topics. However, in practice, many document collections do not have so many documents. Given a small number of documents, the classic topic model LDA generates very poor topics. Even with a large volume of data, unsupervised learning of topic models can still produce unsatisfactory results. In recently years, knowledge-based topic models have been proposed, which ask human users to provide some prior domain knowledge to guide the model to produce better topics. Our research takes a radically different approach. We propose to learn as humans do, i.e., retaining the results learned in the past and using them to help future learning. When faced with a new task, we first mine some reliable (prior) knowledge from the past learning/modeling results and then use it to guide the model inference to generate more coherent topics. This approach is possible because of the big data readily available on the Web. The proposed algorithm mines two forms of knowledge: must-link (meaning that two words should be in the same topic) and cannot-link (meaning that two words should not be in the same topic). It also deals with two problems of the automatically mined knowledge, i.e., wrong knowledge and knowledge transitivity. Experimental results using review documents from 100 product domains show that the proposed approach makes dramatic improvements over state-of-the-art baselines.

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150 citations

Cites methods from "Latent dirichlet allocation"

...This section evaluates the proposed AMC model and compares it with five state-of-the-art baseline models: LDA [4]: The classic unsupervised topic model....
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...Topic models, such as LDA [4], pLSA [12] and their extensions, have been popularly used for topic extraction from...
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Journal Article•DOI•

Virtually in this together – how web-conferencing systems enabled a new virtual togetherness during the COVID-19 crisis

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Janine Hacker¹, Jan vom Brocke¹, Joshua Handali¹, Markus Otto¹, Johannes Schneider¹ - Show less +1 more•Institutions (1)

University of Liechtenstein¹

29 Sep 2020-European Journal of Information Systems

TL;DR: The argument is that WCS emerged as a social technology that led to a new virtual togetherness by facilitating access to everyday activities and contacts that were “locked away” because of COVID-19-mitigation efforts.

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Abstract: Regulations to contain the spread of COVID-19 have affected corporations, institutions, and individuals to a degree that most people have never seen before. Information systems researchers have ini...

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150 citations

Journal Article•DOI•

Dynamic User Modeling in Social Media Systems

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Hongzhi Yin¹, Bin Cui², Ling Chen³, Zhiting Hu⁴, Xiaofang Zhou¹ - Show less +1 more•Institutions (4)

University of Queensland¹, Peking University², University of Technology, Sydney³, Carnegie Mellon University⁴

09 Mar 2015-ACM Transactions on Information Systems

TL;DR: To speed up the process of producing the top-k recommendations from large-scale social media data, an efficient query-processing technique is developed to support the proposed temporal context-aware recommender system (TCARS), and an item-weighting scheme is proposed to enable them to favor items that better represent topics related to user interests and topicsrelated to temporal context.

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Abstract: Social media provides valuable resources to analyze user behaviors and capture user preferences. This article focuses on analyzing user behaviors in social media systems and designing a latent class statistical mixture model, named temporal context-aware mixture model (TCAM), to account for the intentions and preferences behind user behaviors. Based on the observation that the behaviors of a user in social media systems are generally influenced by intrinsic interest as well as the temporal context (e.g., the public's attention at that time), TCAM simultaneously models the topics related to users' intrinsic interests and the topics related to temporal context and then combines the influences from the two factors to model user behaviors in a unified way. Considering that users' interests are not always stable and may change over time, we extend TCAM to a dynamic temporal context-aware mixture model (DTCAM) to capture users' changing interests. To alleviate the problem of data sparsity, we exploit the social and temporal correlation information by integrating a social-temporal regularization framework into the DTCAM model. To further improve the performance of our proposed models (TCAM and DTCAM), an item-weighting scheme is proposed to enable them to favor items that better represent topics related to user interests and topics related to temporal context, respectively. Based on our proposed models, we design a temporal context-aware recommender system (TCARS). To speed up the process of producing the top-k recommendations from large-scale social media data, we develop an efficient query-processing technique to support TCARS. Extensive experiments have been conducted to evaluate the performance of our models on four real-world datasets crawled from different social media sites. The experimental results demonstrate the superiority of our models, compared with the state-of-the-art competitor methods, by modeling user behaviors more precisely and making more effective and efficient recommendations.

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150 citations

Cites background or methods from "Latent dirichlet allocation"

...We adopt the concept of topic from the .eld of text mining [Mei et al. 2008; Blei et al. 2003], and it is de.ned as follows....
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...On-line LDA: Adaptive topic models for mining text streams with applications to topic detection and tracking....
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...As shown in Figure 2, TCAM is a latent class statistical mixture model that simultaneously models the topics [Blei et al. 2003] related to users intrinsic interests and the topics related to the temporal context and then combines the in.uences from the user interest and the temporal context to…...
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...Soft-constraint based online LDA for community recommendation....
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...While traditional topic models, such as LDA [Blei et al. 2003] and PLSA [Hofmann 1999], do not address the temporal information in a document corpus, a number of temporal topic models have been proposed to consider topic evolution over time....
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Journal Article•DOI•

A Combination Approach to Web User Profiling

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Jie Tang¹, Limin Yao², Duo Zhang³, Jing Zhang¹•Institutions (3)

Tsinghua University¹, University of Massachusetts Amherst², University of Illinois at Urbana–Champaign³

01 Dec 2010-ACM Transactions on Knowledge Discovery From Data

TL;DR: This article formalizes the profiling problem as several subtasks: profile extraction, profile integration, and user interest discovery, and proposes a combination approach to deal with the profiling tasks.

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Abstract: In this article, we study the problem of Web user profiling, which is aimed at finding, extracting, and fusing the “semantic”-based user profile from the Web. Previously, Web user profiling was often undertaken by creating a list of keywords for the user, which is (sometimes even highly) insufficient for main applications. This article formalizes the profiling problem as several subtasks: profile extraction, profile integration, and user interest discovery. We propose a combination approach to deal with the profiling tasks. Specifically, we employ a classification model to identify relevant documents for a user from the Web and propose a Tree-Structured Conditional Random Fields (TCRF) to extract the profile information from the identified documents; we propose a unified probabilistic model to deal with the name ambiguity problem (several users with the same name) when integrating the profile information extracted from different sources; finally, we use a probabilistic topic model to model the extracted user profiles, and construct the user interest model. Experimental results on an online system show that the combination approach to different profiling tasks clearly outperforms several baseline methods. The extracted profiles have been applied to expert finding, an important application on the Web. Experiments show that the accuracy of expert finding can be improved (ranging from +6p to +26p in terms of MAP) by taking advantage of the profiles.

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150 citations

Cites methods from "Latent dirichlet allocation"

...Blei et al. [2003] introduced a new semantically consistent topic model, Latent Dirichlet Allocation (LDA)....
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...Modeling the different information sources can be done in many different ways, for example, using the state-of-the-art language model (LM) [BaezaYates and Ribeiro-Neto 1999] or using a separated pLSI [Hofmann 1999] or LDA [Blei et al. 2003] for each type of object....
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...A variety of algorithms have been proposed to conduct approximate inference, for example, variational EM methods [Blei et al. 2003], Gibbs sampling [Grif.ths and Steyvers 2004; Steyvers et al. 2004], and expectation propagation [Grif.ths and Steyvers 2004; Minka 2003]....
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...Modeling the different information sources can be done in many different ways, for example, using the state-of-the-art language model (LM) [Baeza-Yates and Ribeiro-Neto 1999] or using a separated pLSI [Hofmann 1999] or LDA [Blei et al. 2003] for each type of object....
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...Inspired by the distributed inference for LDA [Newman et al. 2007], we can implement a distributed inference algorithm over multiple processors for the proposed models....
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Journal Article•DOI•

Combining machine-learning topic models and spatiotemporal analysis of social media data for disaster footprint and damage assessment

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Bernd Resch¹, Florian Usländer¹, Clemens Havas¹•Institutions (1)

University of Salzburg¹

04 Jul 2018-Cartography and Geographic Information Science

TL;DR: An approach to analyze social media posts to assess the footprint of and the damage caused by natural disasters through combining machine-learning techniques (Latent Dirichlet Allocation) for semantic information extraction with spatial and temporal analysis (local spatial autocorrelation) for hot spot detection.

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Abstract: Current disaster management procedures to cope with human and economic losses and to manage a disaster’s aftermath suffer from a number of shortcomings like high temporal lags or limited temporal a...

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150 citations

Cites methods from "Latent dirichlet allocation"

...Machine-learning for Extracting Semantic Information from Tweets: Topic Modeling with Cascading LDA To extract topics from our Twitter dataset, we use the LDA model (Blei et al., 2003), as shown in Figure 5....
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References

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Handbook of mathematical functions : with formulas, graphs, and mathematical tables

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Milton Abramowitz, Irene A. Stegun

01 Jan 1970

17,608 citations

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Bayesian Data Analysis

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Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari, Donald B. Rubin¹ - Show less +2 more•Institutions (1)

University of California, Irvine¹

01 Jan 1995

TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.

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Abstract: FUNDAMENTALS OF BAYESIAN INFERENCE Probability and Inference Single-Parameter Models Introduction to Multiparameter Models Asymptotics and Connections to Non-Bayesian Approaches Hierarchical Models FUNDAMENTALS OF BAYESIAN DATA ANALYSIS Model Checking Evaluating, Comparing, and Expanding Models Modeling Accounting for Data Collection Decision Analysis ADVANCED COMPUTATION Introduction to Bayesian Computation Basics of Markov Chain Simulation Computationally Efficient Markov Chain Simulation Modal and Distributional Approximations REGRESSION MODELS Introduction to Regression Models Hierarchical Linear Models Generalized Linear Models Models for Robust Inference Models for Missing Data NONLINEAR AND NONPARAMETRIC MODELS Parametric Nonlinear Models Basic Function Models Gaussian Process Models Finite Mixture Models Dirichlet Process Models APPENDICES A: Standard Probability Distributions B: Outline of Proofs of Asymptotic Theorems C: Computation in R and Stan Bibliographic Notes and Exercises appear at the end of each chapter.

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16,079 citations

"Latent dirichlet allocation" refers background in this paper

...Finally, Griffiths and Steyvers (2002) have presented a Markov chain Monte Carlo algorithm for LDA....
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...Structures similar to that shown in Figure 1 are often studied in Bayesian statistical modeling, where they are referred to ashierarchical models(Gelman et al., 1995), or more precisely asconditionally independent hierarchical models(Kass and Steffey, 1989)....
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...Structures similar to that shown in Figure 1 are often studied in Bayesian statistical modeling, where they are referred to as hierarchical models (Gelman et al., 1995), or more precisely as conditionally independent hierarchical models (Kass and Steffey, 1989)....
[...]

Journal Article•DOI•

Indexing by Latent Semantic Analysis

[...]

Scott Deerwester¹, Susan T. Dumais², George W. Furnas², Thomas K. Landauer², Richard A. Harshman³ - Show less +1 more•Institutions (3)

University of Chicago¹, Telcordia Technologies², University of Western Ontario³

01 Sep 1990-Journal of the Association for Information Science and Technology

TL;DR: A new method for automatic indexing and retrieval to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries.

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Abstract: A new method for automatic indexing and retrieval is described. The approach is to take advantage of implicit higher-order structure in the association of terms with documents (“semantic structure”) in order to improve the detection of relevant documents on the basis of terms found in queries. The particular technique used is singular-value decomposition, in which a large term by document matrix is decomposed into a set of ca. 100 orthogonal factors from which the original matrix can be approximated by linear combination. Documents are represented by ca. 100 item vectors of factor weights. Queries are represented as pseudo-document vectors formed from weighted combinations of terms, and documents with supra-threshold cosine values are returned. initial tests find this completely automatic method for retrieval to be promising.

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12,443 citations

"Latent dirichlet allocation" refers methods in this paper

...To address these shortcomings, IR researchers have proposed several other dimensionality reduction techniques, most notably latent semantic indexing (LSI) (Deerwester et al., 1990)....
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...To address these shortcomings, IR researchers have proposed several other dimensionality reduction techniques, most notablylatent semantic indexing (LSI)(Deerwester et al., 1990)....
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Book•

Introduction to Modern Information Retrieval

[...]

Gerard Salton, Michael J. McGill

01 Jan 1983

TL;DR: Reading is a need and a hobby at once and this condition is the on that will make you feel that you must read.

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Abstract: Some people may be laughing when looking at you reading in your spare time. Some may be admired of you. And some may want be like you who have reading hobby. What about your own feel? Have you felt right? Reading is a need and a hobby at once. This condition is the on that will make you feel that you must read. If you know are looking for the book enPDFd introduction to modern information retrieval as the choice of reading, you can find here.

...read moreread less

12,059 citations

"Latent dirichlet allocation" refers background or methods in this paper

...In the populartf-idf scheme (Salton and McGill, 1983), a basic vocabulary of “words” or “terms” is chosen, and, for each document in the corpus, a count is formed of the number of occurrences of each word....
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...We report results in document modeling, text classification, and collaborative filtering, comparing to a mixture of unigrams model and the probabilistic LSI model....
[...]

Book•

Theory of probability

[...]

Harold Jeffreys, R. Bruce Lindsay

01 Jan 1939

TL;DR: In this paper, the authors introduce the concept of direct probabilities, approximate methods and simplifications, and significant importance tests for various complications, including one new parameter, and various complications for frequency definitions and direct methods.

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Abstract: 1. Fundamental notions 2. Direct probabilities 3. Estimation problems 4. Approximate methods and simplifications 5. Significance tests: one new parameter 6. Significance tests: various complications 7. Frequency definitions and direct methods 8. General questions

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7,086 citations