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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Journal Article•DOI•

Topic modeling and sentiment analysis of global climate change tweets

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Biraj Dahal¹, Sathish A. P. Kumar², Zhenlong Li³•Institutions (3)

Clemson University¹, Coastal Carolina University², University of South Carolina³

07 Oct 2019-Social Network Analysis and Mining

TL;DR: A large dataset of geotagged tweets containing certain keywords relating to climate change is analyzed using volume analysis and text mining techniques such as topic modeling and sentiment analysis to compare and contrast the nature of climate change discussion between different countries and over time.

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Abstract: Social media websites can be used as a data source for mining public opinion on a variety of subjects including climate change. Twitter, in particular, allows for the evaluation of public opinion across both time and space because geotagged tweets include timestamps and geographic coordinates (latitude/longitude). In this study, a large dataset of geotagged tweets containing certain keywords relating to climate change is analyzed using volume analysis and text mining techniques such as topic modeling and sentiment analysis. Latent Dirichlet allocation was applied for topic modeling to infer the different topics of discussion, and Valence Aware Dictionary and sEntiment Reasoner was applied for sentiment analysis to determine the overall feelings and attitudes found in the dataset. These techniques are used to compare and contrast the nature of climate change discussion between different countries and over time. Sentiment analysis shows that the overall discussion is negative, especially when users are reacting to political or extreme weather events. Topic modeling shows that the different topics of discussion on climate change are diverse, but some topics are more prevalent than others. In particular, the discussion of climate change in the USA is less focused on policy-related topics than other countries.

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151 citations

Posted Content•

Mixture models with a prior on the number of components

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Jeffrey W. Miller¹, Matthew T. Harrison²•Institutions (2)

Harvard University¹, Brown University²

22 Feb 2015-arXiv: Methodology

TL;DR: It turns out that many of the essential properties of DPMs are also exhibited by MFMs, and the MFM analogues are simple enough that they can be used much like the corresponding DPM properties; this simplifies the implementation of MFMs and can substantially improve mixing.

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Abstract: A natural Bayesian approach for mixture models with an unknown number of components is to take the usual finite mixture model with Dirichlet weights, and put a prior on the number of components---that is, to use a mixture of finite mixtures (MFM). While inference in MFMs can be done with methods such as reversible jump Markov chain Monte Carlo, it is much more common to use Dirichlet process mixture (DPM) models because of the relative ease and generality with which DPM samplers can be applied. In this paper, we show that, in fact, many of the attractive mathematical properties of DPMs are also exhibited by MFMs---a simple exchangeable partition distribution, restaurant process, random measure representation, and in certain cases, a stick-breaking representation. Consequently, the powerful methods developed for inference in DPMs can be directly applied to MFMs as well. We illustrate with simulated and real data, including high-dimensional gene expression data.

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151 citations

Cites background or methods from "Latent dirichlet allocation"

...Mixture models are used in a wide range of applications, including population structure (Pritchard, Stephens, and Donnelly 2000), document modeling (Blei, Ng, and Jordan 2003), speaker recognition (Reynolds, Quatieri, and Dunn 2000), computer vision (Stauffer and Grimson 1999), phylogenetics (Pagel…...
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..., 2000), document modeling (Blei et al., 2003), speaker recognition (Reynolds et al....
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Journal Article•DOI•

Unstructured data in marketing

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Bitty Balducci¹, Detelina Marinova¹•Institutions (1)

University of Missouri¹

12 Jun 2018-Journal of the Academy of Marketing Science

TL;DR: In this paper, a unified definition and conceptualization of unstructured data (UD) in marketing is proposed, bridging disjoint literature with an organizing framework that synthesizes various subsets of UD relevant for marketing management through an integrative review.

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Abstract: The rise of unstructured data (UD), propelled by novel technologies, is reshaping markets and the management of marketing activities. Yet these increased data remain mostly untapped by many firms, suggesting the potential for further research developments. The integrative framework proposed in this study addresses the nature of UD and pursues theoretical richness and computational advancements by integrating insights from other disciplines. This article makes three main contributions to the literature by (1) offering a unifying definition and conceptualization of UD in marketing; (2) bridging disjoint literature with an organizing framework that synthesizes various subsets of UD relevant for marketing management through an integrative review; and (3) identifying substantive, computational, and theoretical gaps in extant literature and ways to leverage interdisciplinary knowledge to advance marketing research by applying UD analyses to underdeveloped areas.

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151 citations

Cites methods from "Latent dirichlet allocation"

...…design preferences by extracting audio fromUGC in Bunboxing^ videos and applying IBMWatson’s Speech to Text (IBM2017) program to convert the audio into text transcriptions, which then could be classified and summarized according to the topic content with the LDA algorithm (Blei et al. 2003)....
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Journal Article•DOI•

Text Mining for Information Systems Researchers: An Annotated Topic Modeling Tutorial

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Stefan Debortoli¹, Oliver Müller², Iris A. Junglas³, Jan vom Brocke¹•Institutions (3)

University of Liechtenstein¹, IT University of Copenhagen², Florida State University³

01 Jan 2016-Communications of The Ais

TL;DR: This tutorial showcases the use of probabilistic topic modeling via Latent Dirichlet Allocation, an unsupervised text mining technique, in combination with a LASSO multinomial logistic regression to explain user satisfaction with an IT artifact by automatically analyzing more than 12,000 online customer reviews.

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Abstract: It is estimated that more than 80 percent of today’s data is stored in unstructured form (e.g., text, audio, image, video); and much of it is expressed in rich and ambiguous natural language. Traditionally, the analysis of natural language has prompted the use of qualitative data analysis approaches, such as manual coding. Yet, the size of text data sets obtained from the Internet makes manual analysis virtually impossible. In this tutorial, we discuss the challenges encountered when applying automated text-mining techniques in information systems research. In particular, we showcase the use of probabilistic topic modeling via Latent Dirichlet Allocation, an unsupervised text mining technique, in combination with a LASSO multinomial logistic regression to explain user satisfaction with an IT artifact by automatically analyzing more than 12,000 online customer reviews. For fellow information systems researchers, this tutorial provides some guidance for conducting text mining studies on their own and for evaluating the quality of others.

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151 citations

Cites background or methods from "Latent dirichlet allocation"

...In order to overcome these shortcomings, probabilistic LSA (pLSA) (Hofmann, 1999) and Latent Dirichlet Allocation (LDA) (Blei et al., 2003; Blei, 2012) have been developed as extensions to the classic LSA idea....
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...The most crucial LDA parameter is the number of topics to be extracted (Blei et al., 2003; Boyd-Graber et al., 2014)....
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...The core idea behind LDA, first proposed by Blei et al. (2003), is an imaginary generative process that assumes that authors compose D documents by first choosing a discrete distribution of T topics to write about, and then drawing W words from a discrete distribution of words that are typical for…...
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...Apart from descriptive purposes, topic models can also be used for explanatory or predictive purposes (Blei et al., 2003)....
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Journal Article•DOI•

Dirichlet Process Mixtures of Generalized Linear Models

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Lauren A. Hannah¹, David M. Blei¹, Warren B. Powell¹•Institutions (1)

Princeton University¹

01 Feb 2011-Journal of Machine Learning Research

TL;DR: The authors proposed Dirichlet process mixtures of generalized linear models (DP-GLM) for nonparametric regression, which allows both continuous and categorical inputs and can model the same class of responses that can be modeled with a generalized linear model.

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Abstract: We propose Dirichlet Process mixtures of Generalized Linear Models (DP-GLM), a new class of methods for nonparametric regression. Given a data set of input-response pairs, the DP-GLM produces a global model of the joint distribution through a mixture of local generalized linear models. DP-GLMs allow both continuous and categorical inputs, and can model the same class of responses that can be modeled with a generalized linear model. We study the properties of the DP-GLM, and show why it provides better predictions and density estimates than existing Dirichlet process mixture regression models. We give conditions for weak consistency of the joint distribution and pointwise consistency of the regression estimate.

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151 citations

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References

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Book•

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

[...]

Milton Abramowitz, Irene A. Stegun

01 Jan 1970

17,608 citations

Book•

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)....
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Journal Article•DOI•

Indexing by Latent Semantic Analysis

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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.

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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

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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