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•

Random field topic model for semantic region analysis in crowded scenes from tracklets

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Bolei Zhou¹, Xiaogang Wang¹, Xiaoou Tang¹•Institutions (1)

The Chinese University of Hong Kong¹

20 Jun 2011

TL;DR: Experiments on a large scale data set show that the proposed Random Field Topic model outperforms state-of-the-art methods both on qualitative results of learning semantic regions and on quantitative results of clustering tracklets.

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Abstract: In this paper, a Random Field Topic (RFT) model is proposed for semantic region analysis from motions of objects in crowded scenes. Different from existing approaches of learning semantic regions either from optical flows or from complete trajectories, our model assumes that fragments of trajectories (called tracklets) are observed in crowded scenes. It advances the existing Latent Dirichlet Allocation topic model, by integrating the Markov random fields (MR-F) as prior to enforce the spatial and temporal coherence between tracklets during the learning process. Two kinds of MRF, pairwise MRF and the forest of randomly spanning trees, are defined. Another contribution of this model is to include sources and sinks as high-level semantic prior, which effectively improves the learning of semantic regions and the clustering of tracklets. Experiments on a large scale data set, which includes 40, 000+ tracklets collected from the crowded New York Grand Central station, show that our model outperforms state-of-the-art methods both on qualitative results of learning semantic regions and on quantitative results of clustering tracklets.

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162 citations

Cites background or methods from "Latent dirichlet allocation"

...It advances the Latent Dirichlet Allocation topic model (LDA) [2], by integrating MRF as prior to enforce the spatial and temporal coherence between tracklets during the learning process....
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...They serve as priors of Dirichlet distributions to avoid singularity of the model, the general discussion for the influence of the hyper-parameters on learning topic model could be found in [2]....
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Journal Article•DOI•

Understanding the use of Virtual Reality in Marketing: a text mining-based review

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Sandra Loureiro¹, João Guerreiro¹, Sara Eloy¹, Daniela Langaro¹, Padma Panchapakesan² - Show less +1 more•Institutions (2)

ISCTE – University Institute of Lisbon¹, Taylors University²

01 Jul 2019-Journal of Business Research

TL;DR: A text-mining approach using a Bayesian statistical topic model called latent Dirichlet allocation is employed to conduct a comprehensive analysis of 150 articles from 115 journals, revealing seven relevant topics.

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162 citations

Journal Article•DOI•

Aspect extraction in sentiment analysis: comparative analysis and survey

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Toqir Ahmad Rana¹, Yu-N Cheah¹•Institutions (1)

Universiti Sains Malaysia¹

01 Dec 2016-Artificial Intelligence Review

TL;DR: A comprehensive comparative analysis is conducted among different approaches of aspect extraction, which not only elaborates the performance of any technique but also guides the reader to compare the accuracy with other state-of-the-art and most recent approaches.

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Abstract: Sentiment analysis (SA) has become one of the most active and progressively popular areas in information retrieval and text mining due to the expansion of the World Wide Web (WWW). SA deals with the computational treatment or the classification of user's sentiments, opinions and emotions hidden within the text. Aspect extraction is the most vital and extensively explored phase of SA to carry out the classification of sentiments in precise manners. During the last decade, enormous number of research has focused on identifying and extracting aspects. Therefore, in this survey, a comprehensive overview has been attempted for different aspect extraction techniques and approaches. These techniques have been categorized in accordance with the adopted approach. Despite being a traditional survey, a comprehensive comparative analysis is conducted among different approaches of aspect extraction, which not only elaborates the performance of any technique but also guides the reader to compare the accuracy with other state-of-the-art and most recent approaches.

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162 citations

Cites methods from "Latent dirichlet allocation"

...Despite of dictionary-based approaches, some researchers have used LDA (Blei et al. 2003) to group similar aspects....
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...Although, we have not included topic modeling techniques (e.g. Latent Dirichlet Allocation (LDA)) for aspect extraction in this study, because the comparison of topic modeling approaches with the techniques presented in this review is not conceivable, due to the unavailability of precise results....
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...They also considered noun/noun phrases as aspects and with incorporating LDA generated a candidate aspect list....
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...Following similar Table 2 Summary of semi-supervised techniques References Year Model Algorithm used Domain Language Implicit aspect Wang and Wang (2008) 2008 BST2* Bootstrapping Product Chinese Yes Hai et al. (2012) 2012 LRTBOOT Bootstrapping Cell phone Chinese No Restaurant Hai et al. (2012) 2012 LSABOOT Bootstrapping Cell phone Chinese No Restaurant Zhao et al. (2014) 2014 BST3* Bootstrapping Product Chinese No Wu et al. (2009) 2009 D-Parser1* Dependency Parser Product English No Wei et al. (2010) 2010 SPE Semantic-based Product English No Yu et al. (2011a) 2011 D-parser2* Dependency Parser Product English No Qiu et al. (2011) 2011 DP Double Propagation Product English Yes Zhang et al. (2010) 2010 DP1* Double Propagation Product English Yes Ma et al. (2013) 2013 Lexicon-LDA* LDA + lexicon Product Chinese No Liu et al. (2013b) 2013 WAM Word alignment Product English No Liu et al. (2015) 2014 PSWAM Word Alignment Product English No Xu et al. (2013a) 2013 GP-based* Graph-based Product English No Samha et al. (2014) 2014 Lexicon-FT* Frequent tags + lexicon Product English No Yan et al. (2015) 2015 EXPRS Page rank + Lexicon-based Product Chinese Yes approach, Hai et al. (2012) proposed likelihood ratio set (LRTBOOT) and latent semantic analysis (LSABOOT) bootstrapping methods....
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...Xu et al. (2015) used LDA to construct explicit topic model and then incorporating must-link, cannot-link and relevance-based prior knowledge with explicit topic model to extract implicit aspects....
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Proceedings Article•DOI•

Learning evolving and emerging topics in social media: a dynamic nmf approach with temporal regularization

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Ankan Saha¹, Vikas Sindhwani²•Institutions (2)

University of Chicago¹, IBM²

08 Feb 2012

TL;DR: An online nonnegative matrix factorizations framework is proposed to capture the evolution and emergence of themes in unstructured text under a novel temporal regularization framework and is able to rapidly capture emerging themes, track existing topics over time while maintaining temporal consistency and continuity in user views.

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Abstract: As massive repositories of real-time human commentary, social media platforms have arguably evolved far beyond passive facilitation of online social interactions. Rapid analysis of information content in online social media streams (news articles, blogs,tweets etc.) is the need of the hour as it allows business and government bodies to understand public opinion about products and policies. In most of these settings, data points appear as a stream of high dimensional feature vectors. Guided by real-world industrial deployment scenarios, we revisit the problem of online learning of topics from streaming social media content. On one hand, the topics need to be dynamically adapted to the statistics of incoming datapoints, and on the other hand, early detection of rising new trends is important in many applications. We propose an online nonnegative matrix factorizations framework to capture the evolution and emergence of themes in unstructured text under a novel temporal regularization framework. We develop scalable optimization algorithms for our framework, propose a new set of evaluation metrics, and report promising empirical results on traditional TDT tasks as well as streaming Twitter data. Our system is able to rapidly capture emerging themes, track existing topics over time while maintaining temporal consistency and continuity in user views, and can be explicitly configured to bound the amount of information being presented to the user.

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162 citations

Journal Article•DOI•

Aligning Where to See and What to Tell: Image Captioning with Region-Based Attention and Scene-Specific Contexts

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Kun Fu¹, Junqi Jin¹, Runpeng Cui², Fei Sha³, Changshui Zhang² - Show less +1 more•Institutions (3)

University of Southern California¹, Tsinghua University², University of California, Los Angeles³

01 Dec 2017-IEEE Transactions on Pattern Analysis and Machine Intelligence

TL;DR: This paper proposes an image captioning system that exploits the parallel structures between images and sentences and makes another novel modeling contribution by introducing scene-specific contexts that capture higher-level semantic information encoded in an image.

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Abstract: Recent progress on automatic generation of image captions has shown that it is possible to describe the most salient information conveyed by images with accurate and meaningful sentences. In this paper, we propose an image captioning system that exploits the parallel structures between images and sentences. In our model, the process of generating the next word, given the previously generated ones, is aligned with the visual perception experience where the attention shifts among the visual regions—such transitions impose a thread of ordering in visual perception. This alignment characterizes the flow of latent meaning, which encodes what is semantically shared by both the visual scene and the text description. Our system also makes another novel modeling contribution by introducing scene-specific contexts that capture higher-level semantic information encoded in an image. The contexts adapt language models for word generation to specific scene types. We benchmark our system and contrast to published results on several popular datasets, using both automatic evaluation metrics and human evaluation. We show that either region-based attention or scene-specific contexts improves systems without those components. Furthermore, combining these two modeling ingredients attains the state-of-the-art performance.

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162 citations

Cites methods from "Latent dirichlet allocation"

...Concretely, for the first step, we use Latent Dirichlet Allocation (LDA) [33] to model the corpus of all the captions in the training dataset of MSCOCO....
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...The training samples for this regressor are the images from the same training dataset of MSCOCOwith the target outputs being the LDA-inferred scene vectors....
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...Note that, representing images with global feature vectors and using the scene classifier provide an effective way to categorize test images where captions are not available (thus scene vectors cannot be inferred from LDA)....
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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)....
[...]
...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