Latent dirichlet allocation

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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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Posted Content•

Is the Sample Good Enough? Comparing Data from Twitter's Streaming API with Twitter's Firehose

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Fred Morstatter¹, Jürgen Pfeffer², Huan Liu¹, Kathleen M. Carley²•Institutions (2)

Arizona State University¹, Carnegie Mellon University²

21 Jun 2013-arXiv: Social and Information Networks

TL;DR: Data collected using Twitter's sampled API service is compared with data collected using the full, albeit costly, Firehose stream that includes every single published tweet to help researchers and practitioners understand the implications of using the Streaming API.

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Abstract: Twitter is a social media giant famous for the exchange of short, 140-character messages called "tweets". In the scientific community, the microblogging site is known for openness in sharing its data. It provides a glance into its millions of users and billions of tweets through a "Streaming API" which provides a sample of all tweets matching some parameters preset by the API user. The API service has been used by many researchers, companies, and governmental institutions that want to extract knowledge in accordance with a diverse array of questions pertaining to social media. The essential drawback of the Twitter API is the lack of documentation concerning what and how much data users get. This leads researchers to question whether the sampled data is a valid representation of the overall activity on Twitter. In this work we embark on answering this question by comparing data collected using Twitter's sampled API service with data collected using the full, albeit costly, Firehose stream that includes every single published tweet. We compare both datasets using common statistical metrics as well as metrics that allow us to compare topics, networks, and locations of tweets. The results of our work will help researchers and practitioners understand the implications of using the Streaming API.

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848 citations

Cites methods from "Latent dirichlet allocation"

...Since LDA’s topics have no implicit orderings we first must match them based upon the similarity of the words in the distribution....
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...In the case of LDA we find a significant increase in the accuracy of LDA with the randomly sampled data over the data from the Streaming API....
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...We compare the topics drawn from the Streaming data with those drawn from the Firehose data using a widely-used topic modeling algorithm, latent Dirichlet allocation (LDA) (Blei, Ng, and Jordan 2003)....
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...To get a sense of how the topics found in the Streaming data compare with those found with random samples, we compare with topics found by running LDA on random subsamples of the Firehose data....
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...We also employed LDA to extract topics from the text....
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Proceedings Article•DOI•

LDAvis: A method for visualizing and interpreting topics

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Carson Sievert, Kenneth E. Shirley¹•Institutions (1)

AT&T¹

01 Jan 2014

TL;DR: LDAvis, a web-based interactive visualization of topics estimated using Latent Dirichlet Allocation that is built using a combination of R and D3, and a novel method for choosing which terms to present to a user to aid in the task of topic interpretation is proposed.

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Abstract: We present LDAvis, a web-based interactive visualization of topics estimated using Latent Dirichlet Allocation that is built using a combination of R and D3. Our visualization provides a global view of the topics (and how they differ from each other), while at the same time allowing for a deep inspection of the terms most highly associated with each individual topic. First, we propose a novel method for choosing which terms to present to a user to aid in the task of topic interpretation, in which we define the relevance of a term to a topic. Second, we present results from a user study that suggest that ranking terms purely by their probability under a topic is suboptimal for topic interpretation. Last, we describe LDAvis, our visualization system that allows users to flexibly explore topic-term relationships using relevance to better understand a fitted LDA model.

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836 citations

Cites methods from "Latent dirichlet allocation"

...Blei and Lafferty (2009) developed “Turbo Topics”, a method of identifying n-grams within LDAinferred topics that, when listed in decreasing order of probability, provide users with extra information about the usage of terms within topics....
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...Blei and Lafferty (2009) developed “Turbo Topics”, a method of identifying n-grams within LDAinferred topics that, when listed in decreasing order of probability, provide users with extra information about the usage of terms within topics. This two-stage process yields good results on experimental data, although the resulting output is still simply a ranked list containing a mixture of terms and n-grams, and the usefulness of the method for topic interpretation was not tested in a user study. Newman et al. (2010) describe a method for ranking terms within topics to aid interpretability called Pointwise Mutual Information (PMI) ranking....
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...Blei and Lafferty (2009) developed “Turbo Topics”, a method of identifying n-grams within LDAinferred topics that, when listed in decreasing order of probability, provide users with extra information about the usage of terms within topics. This two-stage process yields good results on experimental data, although the resulting output is still simply a ranked list containing a mixture of terms and n-grams, and the usefulness of the method for topic interpretation was not tested in a user study. Newman et al. (2010) describe a method for ranking terms within topics to aid interpretability called Pointwise Mutual Information (PMI) ranking. Under PMI ranking of terms, each of the ten most probable terms within a topic are ranked in decreasing order of approximately how often they occur in close proximity to the nine other most probable terms from that topic in some large, external “reference” corpus, such as Wikipedia or Google n-grams. Although this method correlated highly with human judgments of term importance within topics, it does not easily generalize to topic models fit to corpora that don’t have a readily available external source of word co-occurrences. In contrast, Taddy (2011) uses an intrinsic measure to rank terms within topics: a quantity called lift, defined as the ratio of a term’s probability within a topic to its marginal probability across the corpus....
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Proceedings Article•

Automatic Evaluation of Topic Coherence

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David Newman¹, Jey Han Lau², Karl Grieser², Timothy Baldwin²•Institutions (2)

University of California, Irvine¹, University of Melbourne²

02 Jun 2010

TL;DR: A simple co-occurrence measure based on pointwise mutual information over Wikipedia data is able to achieve results for the task at or nearing the level of inter-annotator correlation, and that other Wikipedia-based lexical relatedness methods also achieve strong results.

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Abstract: This paper introduces the novel task of topic coherence evaluation, whereby a set of words, as generated by a topic model, is rated for coherence or interpretability. We apply a range of topic scoring models to the evaluation task, drawing on WordNet, Wikipedia and the Google search engine, and existing research on lexical similarity/relatedness. In comparison with human scores for a set of learned topics over two distinct datasets, we show a simple co-occurrence measure based on pointwise mutual information over Wikipedia data is able to achieve results for the task at or nearing the level of inter-annotator correlation, and that other Wikipedia-based lexical relatedness methods also achieve strong results. Google produces strong, if less consistent, results, while our results over WordNet are patchy at best.

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832 citations

Cites background or methods from "Latent dirichlet allocation"

...…collection, in the form of methods such as latent semantic analysis (Deerwester et al., 1990), probabilistic latent semantic analysis (Hofmann, 2001), random projection (Widdows and Ferraro, 2008), and more recently, latent Dirichlet allocation (Blei et al., 2003; Griffiths and Steyvers, 2004)....
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..., 1990), probabilistic latent semantic analysis (Hofmann, 2001), random projection (Widdows and Ferraro, 2008), and more recently, latent Dirichlet allocation (Blei et al., 2003; Griffiths and Steyvers, 2004)....
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...LDA is a Bayesian graphical model for text document collections represented by bags-of-words (see Blei et al. (2003), Griffiths and Steyvers (2004), Buntine and Jakulin (2004))....
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Proceedings Article•DOI•

Joint Deep Modeling of Users and Items Using Reviews for Recommendation

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Lei Zheng¹, Vahid Noroozi¹, Philip S. Yu¹•Institutions (1)

University of Illinois at Chicago¹

02 Feb 2017

TL;DR: A deep model to learn item properties and user behaviors jointly from review text, named Deep Cooperative Neural Networks (DeepCoNN), consists of two parallel neural networks coupled in the last layers.

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Abstract: A large amount of information exists in reviews written by users. This source of information has been ignored by most of the current recommender systems while it can potentially alleviate the sparsity problem and improve the quality of recommendations. In this paper, we present a deep model to learn item properties and user behaviors jointly from review text. The proposed model, named Deep Cooperative Neural Networks (DeepCoNN), consists of two parallel neural networks coupled in the last layers. One of the networks focuses on learning user behaviors exploiting reviews written by the user, and the other one learns item properties from the reviews written for the item. A shared layer is introduced on the top to couple these two networks together. The shared layer enables latent factors learned for users and items to interact with each other in a manner similar to factorization machine techniques. Experimental results demonstrate that DeepCoNN significantly outperforms all baseline recommender systems on a variety of datasets.

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828 citations

Cites background or methods from "Latent dirichlet allocation"

...For LDA, CTR and HFT, the number of topics K is selected from {5, 10, 20, 50, 100} using the validation set....
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...To compare our model with topic modeling based recommender systems, we select three representative models: Latent Dirichlet Allocation (LDA) [5], Collaborative Topic Regression (CTR) [33] and Hidden Factor as Topic (HFT) [17], and (iii) deep recommender systems....
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...Among those topic modeling based models (LDA, CTR and HFT), both HFT-10 and HFT-50 perform better in all three datasets....
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...• LDA:LatentDirichletAllocation is a well-known topic modeling algorithm presented in [5]....
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...We set K = 10 for LDA and CTR....
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Journal Article•DOI•

Information diffusion in online social networks: a survey

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Adrien Guille, Hakim Hacid¹, Cécile Favre, Djamel A. Zighed•Institutions (1)

Bell Labs¹

16 Jul 2013

TL;DR: A survey of representative methods dealing with information diffusion in social networks and a taxonomy that summarizes the state-of-the-art is proposed, intended to help researchers in quickly understanding existing works and possible improvements to bring.

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Abstract: Online social networks play a major role in the spread of information at very large scale. A lot of effort have been made in order to understand this phenomenon, ranging from popular topic detection to information diffusion modeling, including influential spreaders identification. In this article, we present a survey of representative methods dealing with these issues and propose a taxonomy that summarizes the state-of-the-art. The objective is to provide a comprehensive analysis and guide of existing efforts around information diffusion in social networks. This survey is intended to help researchers in quickly understanding existing works and possible improvements to bring.

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823 citations

Cites background from "Latent dirichlet allocation"

...Latent Dirichlet Allocation [4]) is a statistical generative model that relies on a hierarchical Bayesian network that relates words and mesha l-0 08 48 05 0, v er si on 1 25 J ul 2 01 3...
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...Basically, LDA (i.e. Latent Dirichlet Allocation [4]) is a statistical generative model that relies on a hierarchical Bayesian network that relates words and mes-sages through latent topics....
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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

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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)....
[...]

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.

...read moreread less

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....
[...]

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Theory of probability

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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