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Journal ArticleDOI

Latent dirichlet allocation

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.
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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Citations
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Proceedings ArticleDOI
11 Apr 2016
TL;DR: This paper found that persuasive arguments are characterized by interesting patterns of interaction dynamics, such as participant entry-order and degree of back-and-forth exchange, and that stylistic choices in how the opinion is expressed carry predictive power.
Abstract: Changing someone's opinion is arguably one of the most important challenges of social interaction. The underlying process proves difficult to study: it is hard to know how someone's opinions are formed and whether and how someone's views shift. Fortunately, ChangeMyView, an active community on Reddit, provides a platform where users present their own opinions and reasoning, invite others to contest them, and acknowledge when the ensuing discussions change their original views. In this work, we study these interactions to understand the mechanisms behind persuasion. We find that persuasive arguments are characterized by interesting patterns of interaction dynamics, such as participant entry-order and degree of back-and-forth exchange. Furthermore, by comparing similar counterarguments to the same opinion, we show that language factors play an essential role. In particular, the interplay between the language of the opinion holder and that of the counterargument provides highly predictive cues of persuasiveness. Finally, since even in this favorable setting people may not be persuaded, we investigate the problem of determining whether someone's opinion is susceptible to being changed at all. For this more difficult task, we show that stylistic choices in how the opinion is expressed carry predictive power.

257 citations

Journal ArticleDOI
TL;DR: Novel topic-aware influence-driven propagation models that are more accurate in describing real-world cascades than the standard (i.e., topic-blind) propagation models studied in the literature are introduced.
Abstract: The study of influence-driven propagations in social networks and its exploitation for viral marketing purposes has recently received a large deal of attention. However, regardless of the fact that users authoritativeness, expertise, trust and influence are evidently topic-dependent, the research on social influence has surprisingly largely overlooked this aspect. In this article, we study social influence from a topic modeling perspective. We introduce novel topic-aware influence-driven propagation models that, as we show in our experiments, are more accurate in describing real-world cascades than the standard (i.e., topic-blind) propagation models studied in the literature. In particular, we first propose simple topic-aware extensions of the well-known Independent Cascade and Linear Threshold models. However, these propagation models have a very large number of parameters which could lead to overfitting. Therefore, we propose a different approach explicitly modeling authoritativeness, influence and relevance under a topic-aware perspective. Instead of considering user-to-user influence, the proposed model focuses on user authoritativeness and interests in a topic, leading to a drastic reduction in the number of parameters of the model. We devise methods to learn the parameters of the models from a data set of past propagations. Our experimentation confirms the high accuracy of the proposed models and learning schemes.

257 citations


Additional excerpts

  • ...VI discusses future work....

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Proceedings ArticleDOI
29 Oct 2012
TL;DR: This work proposes a novel semi-supervised approach for detecting profanity-related offensive content in Twitter that exploits linguistic regularities in profane language via statistical topic modeling on a huge Twitter corpus, and detects offensive tweets using automatically these generated features.
Abstract: In this paper, we propose a novel semi-supervised approach for detecting profanity-related offensive content in Twitter. Our approach exploits linguistic regularities in profane language via statistical topic modeling on a huge Twitter corpus, and detects offensive tweets using automatically these generated features. Our approach performs competitively with a variety of machine learning (ML) algorithms. For instance, our approach achieves a true positive rate (TP) of 75.1% over 4029 testing tweets using Logistic Regression, significantly outperforming the popular keyword matching baseline, which has a TP of 69.7%, while keeping the false positive rate (FP) at the same level as the baseline at about 3.77%. Our approach provides an alternative to large scale hand annotation efforts required by fully supervised learning approaches.

256 citations


Cites methods from "Latent dirichlet allocation"

  • ...models from this tweet set via latent dirichlet allocation (LDA) (Blei et al., 2003), a well-known generative topic modeling algorithm....

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  • ...With a set of training tweets as obtained in Algorithm 1, we adopt the latent Dirichlet allocation (LDA) (Blei et al., 2003), a renowned generative probabilistic model for topic discovery, to build the composite topical features....

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Proceedings ArticleDOI
Ding Zhou1, Eren Manavoglu1, Jia Li1, C. Lee Giles1, Hongyuan Zha1 
23 May 2006
TL;DR: Experimental studies on Enron email corpus show that the proposed generative Bayesian models for semantic community discovery in SNs successfully detects the communities of individuals and in addition provides semantic topic descriptions of these communities.
Abstract: The increasing amount of communication between individuals in e-formats (e.g. email, Instant messaging and the Web) has motivated computational research in social network analysis (SNA). Previous work in SNA has emphasized the social network (SN) topology measured by communication frequencies while ignoring the semantic information in SNs. In this paper, we propose two generative Bayesian models for semantic community discovery in SNs, combining probabilistic modeling with community detection in SNs. To simulate the generative models, an EnF-Gibbs sampling algorithm is proposed to address the efficiency and performance problems of traditional methods. Experimental studies on Enron email corpus show that our approach successfully detects the communities of individuals and in addition provides semantic topic descriptions of these communities.

256 citations


Cites background from "Latent dirichlet allocation"

  • ...Related work on document content characterization [1, 7, 11, 21] introduces a set of probabilistic models to simulate the generation of a document....

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  • ...3(a) models documents as generated by a mixture of topics [1]....

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  • ...topic [7, 1]), are modeled as variables in the generative Bayesian network and have been shown to work well for document content characterization....

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01 Mar 2013
TL;DR: Evaluation on three data sets shows that the distributional-based measures outperform the state-of-the-art approach for this task.
Abstract: This paper introduces distributional semantic similarity methods for automatically measuring the coherence of a set of words generated by a topic model. We construct a semantic space to represent each topic word by making use of Wikipedia as a reference corpus to identify context features and collect frequencies. Relatedness between topic words and context features is measured using variants of Pointwise Mutual Information (PMI). Topic coherence is determined by measuring the distance between these vectors computed using a variety of metrics. Evaluation on three data sets shows that the distributional-based measures outperform the state-of-the-art approach for this task.

255 citations


Cites background or methods from "Latent dirichlet allocation"

  • ...Topic modelling is a popular statistical method for (soft) clustering documents (Blei et al., 2003; Deerwester et al., 1990; Hofmann, 1999)....

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  • ...Latent Dirichlet Allocation (LDA) (Blei et al., 2003), one type of topic model, has been widely used in NLP and applied to a range of tasks including word sense disambiguation (Boyd-Graber et al....

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  • ...A total of 300 topics are 1http://dumps.wikimedia.org/enwiki/20120104/ 2We also experimented with different lengths of context windows 3The data set can be downloaded from http://staffwww.dcs.shef.ac.uk/people/N.Aletras/ resources/TopicCoherence300.tar.gz generated by running LDA over three different document collections: • NYT: 47,229 New York Times news articles published between May and December 2010 from the GigaWord corpus....

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  • ...Latent Dirichlet Allocation (LDA) (Blei et al., 2003), one type of topic model, has been widely used in NLP and applied to a range of tasks including word sense disambiguation (Boyd-Graber et al., 2007), multi-document summarisation (Haghighi and Vanderwende, 2009) and generation of comparable…...

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  • ...Latent Dirichlet Allocation (LDA) (Blei et al., 2003), one type of topic model, has been widely used in NLP and applied to a range of tasks including word sense disambiguation (Boyd-Graber et al., 2007), multi-document summarisation (Haghighi and Vanderwende, 2009) and generation of comparable corpora (Preiss, 2012)....

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References
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Book
01 Jan 1995
TL;DR: Detailed notes on Bayesian Computation Basics of Markov Chain Simulation, Regression Models, and Asymptotic Theorems are provided.
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.

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 ArticleDOI
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.
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.

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
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.
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.

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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Book
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.
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

7,086 citations