>> where $n_{ij}$ the number of occurrence of word $j$ under topic $i$, $m_{di}$ is the number of loci in $d$-th individual that originated from population $i$. "After the incident", I started to be more careful not to trip over things. I can use the total number of words from each topic across all documents as the \(\overrightarrow{\beta}\) values. Latent Dirichlet Allocation (LDA), first published in Blei et al. /Subtype /Form \[ The documents have been preprocessed and are stored in the document-term matrix dtm. Xf7!0#1byK!]^gEt?UJyaX~O9y#?9y>1o3Gt-_6I
H=q2 t`O3??>]=l5Il4PW: YDg&z?Si~;^-tmGw59
j;(N?7C'
4om&76JmP/.S-p~tSPk
t $z_{dn}$ is chosen with probability $P(z_{dn}^i=1|\theta_d,\beta)=\theta_{di}$. Update count matrices $C^{WT}$ and $C^{DT}$ by one with the new sampled topic assignment. stream
Labeled LDA can directly learn topics (tags) correspondences. /Subtype /Form The MCMC algorithms aim to construct a Markov chain that has the target posterior distribution as its stationary dis-tribution. /Subtype /Form For the Nozomi from Shinagawa to Osaka, say on a Saturday afternoon, would tickets/seats typically be available - or would you need to book? endobj This means we can create documents with a mixture of topics and a mixture of words based on thosed topics. Why is this sentence from The Great Gatsby grammatical? \phi_{k,w} = { n^{(w)}_{k} + \beta_{w} \over \sum_{w=1}^{W} n^{(w)}_{k} + \beta_{w}} Feb 16, 2021 Sihyung Park The model consists of several interacting LDA models, one for each modality. >> \end{equation} << /S /GoTo /D [6 0 R /Fit ] >> endstream
endobj
145 0 obj
<. To start note that ~can be analytically marginalised out P(Cj ) = Z d~ YN i=1 P(c ij . ceS"D!q"v"dR$_]QuI/|VWmxQDPj(gbUfgQ?~x6WVwA6/vI`jk)8@$L,2}V7p6T9u$:nUd9Xx]? You may be like me and have a hard time seeing how we get to the equation above and what it even means. \end{aligned} << /FormType 1 probabilistic model for unsupervised matrix and tensor fac-torization. (b) Write down a collapsed Gibbs sampler for the LDA model, where you integrate out the topic probabilities m. 7 0 obj Applicable when joint distribution is hard to evaluate but conditional distribution is known. /FormType 1 \] The left side of Equation (6.1) defines the following: Example: I am creating a document generator to mimic other documents that have topics labeled for each word in the doc. /Matrix [1 0 0 1 0 0] Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. Approaches that explicitly or implicitly model the distribution of inputs as well as outputs are known as generative models, because by sampling from them it is possible to generate synthetic data points in the input space (Bishop 2006). \tag{5.1} One-hot encoded so that $w_n^i=1$ and $w_n^j=0, \forall j\ne i$ for one $i\in V$. \end{aligned} << /BBox [0 0 100 100] \]. R::rmultinom(1, p_new.begin(), n_topics, topic_sample.begin()); n_doc_topic_count(cs_doc,new_topic) = n_doc_topic_count(cs_doc,new_topic) + 1; n_topic_term_count(new_topic , cs_word) = n_topic_term_count(new_topic , cs_word) + 1; n_topic_sum[new_topic] = n_topic_sum[new_topic] + 1; # colnames(n_topic_term_count) <- unique(current_state$word), # get word, topic, and document counts (used during inference process), # rewrite this function and normalize by row so that they sum to 1, # names(theta_table)[4:6] <- paste0(estimated_topic_names, ' estimated'), # theta_table <- theta_table[, c(4,1,5,2,6,3)], 'True and Estimated Word Distribution for Each Topic', , . In _init_gibbs(), instantiate variables (numbers V, M, N, k and hyperparameters alpha, eta and counters and assignment table n_iw, n_di, assign). r44D<=+nnj~u/6S*hbD{EogW"a\yA[KF!Vt zIN[P2;&^wSO endobj /Resources 26 0 R /Filter /FlateDecode . /Type /XObject endstream These functions use a collapsed Gibbs sampler to fit three different models: latent Dirichlet allocation (LDA), the mixed-membership stochastic blockmodel (MMSB), and supervised LDA (sLDA). Current popular inferential methods to fit the LDA model are based on variational Bayesian inference, collapsed Gibbs sampling, or a combination of these. 36 0 obj What is a generative model? 11 0 obj 0000011315 00000 n
endobj Gibbs sampling equates to taking a probabilistic random walk through this parameter space, spending more time in the regions that are more likely. This article is the fourth part of the series Understanding Latent Dirichlet Allocation. /Filter /FlateDecode /Shading << /Sh << /ShadingType 2 /ColorSpace /DeviceRGB /Domain [0.0 100.00128] /Coords [0.0 0 100.00128 0] /Function << /FunctionType 3 /Domain [0.0 100.00128] /Functions [ << /FunctionType 2 /Domain [0.0 100.00128] /C0 [1 1 1] /C1 [1 1 1] /N 1 >> << /FunctionType 2 /Domain [0.0 100.00128] /C0 [1 1 1] /C1 [0 0 0] /N 1 >> << /FunctionType 2 /Domain [0.0 100.00128] /C0 [0 0 0] /C1 [0 0 0] /N 1 >> ] /Bounds [ 25.00032 75.00096] /Encode [0 1 0 1 0 1] >> /Extend [false false] >> >> 0000133624 00000 n
Draw a new value $\theta_{1}^{(i)}$ conditioned on values $\theta_{2}^{(i-1)}$ and $\theta_{3}^{(i-1)}$. 0000011924 00000 n
/Length 15 After sampling $\mathbf{z}|\mathbf{w}$ with Gibbs sampling, we recover $\theta$ and $\beta$ with. \begin{aligned} The latter is the model that later termed as LDA. Each day, the politician chooses a neighboring island and compares the populations there with the population of the current island. (2003) to discover topics in text documents. $C_{dj}^{DT}$ is the count of of topic $j$ assigned to some word token in document $d$ not including current instance $i$. $w_{dn}$ is chosen with probability $P(w_{dn}^i=1|z_{dn},\theta_d,\beta)=\beta_{ij}$. _(:g\/?7z-{>jS?oq#%88K=!&t&,]\k /m681~r5>. This value is drawn randomly from a dirichlet distribution with the parameter \(\beta\) giving us our first term \(p(\phi|\beta)\). /Filter /FlateDecode All Documents have same topic distribution: For d = 1 to D where D is the number of documents, For w = 1 to W where W is the number of words in document, For d = 1 to D where number of documents is D, For k = 1 to K where K is the total number of topics. (LDA) is a gen-erative model for a collection of text documents. >> \tag{6.2} \\ 0000002685 00000 n
/BBox [0 0 100 100] Im going to build on the unigram generation example from the last chapter and with each new example a new variable will be added until we work our way up to LDA. /Type /XObject \begin{equation} \sum_{w} n_{k,\neg i}^{w} + \beta_{w}} Experiments \Gamma(n_{k,\neg i}^{w} + \beta_{w}) )-SIRj5aavh ,8pi)Pq]Zb0< gives us an approximate sample $(x_1^{(m)},\cdots,x_n^{(m)})$ that can be considered as sampled from the joint distribution for large enough $m$s. \tag{6.1} 0000014488 00000 n
  Assume that even if directly sampling from it is impossible, sampling from conditional distributions $p(x_i|x_1\cdots,x_{i-1},x_{i+1},\cdots,x_n)$ is possible. ])5&_gd))=m 4U90zE1A5%q=\e%
kCtk?6h{x/| VZ~A#>2tS7%t/{^vr(/IZ9o{9.bKhhI.VM$ vMA0Lk?E[5`y;5uI|# P=\)v`A'v9c?dqiB(OyX3WLon|&fZ(UZi2nu~qke1_m9WYo(SXtB?GmW8__h} Before we get to the inference step, I would like to briefly cover the original model with the terms in population genetics, but with notations I used in the previous articles. A well-known example of a mixture model that has more structure than GMM is LDA, which performs topic modeling. Arjun Mukherjee (UH) I. Generative process, Plates, Notations . \], The conditional probability property utilized is shown in (6.9). They are only useful for illustrating purposes. xP( Pritchard and Stephens (2000) originally proposed the idea of solving population genetics problem with three-level hierarchical model. From this we can infer \(\phi\) and \(\theta\). &\propto (n_{d,\neg i}^{k} + \alpha_{k}) {n_{k,\neg i}^{w} + \beta_{w} \over 0000116158 00000 n
\begin{equation} . Within that setting . Latent Dirichlet allocation Latent Dirichlet allocation (LDA) is a generative probabilistic model of a corpus. 14 0 obj << endobj stream This is our estimated values and our resulting values: The document topic mixture estimates are shown below for the first 5 documents: \[ model operates on the continuous vector space, it can naturally handle OOV words once their vector representation is provided. The les you need to edit are stdgibbs logjoint, stdgibbs update, colgibbs logjoint,colgibbs update. &= \int p(z|\theta)p(\theta|\alpha)d \theta \int p(w|\phi_{z})p(\phi|\beta)d\phi However, as noted by others (Newman et al.,2009), using such an uncol-lapsed Gibbs sampler for LDA requires more iterations to 31 0 obj /ProcSet [ /PDF ] \begin{equation} /Type /XObject Okay. LDA is know as a generative model. xK0 Latent Dirichlet Allocation Using Gibbs Sampling - GitHub Pages In order to use Gibbs sampling, we need to have access to information regarding the conditional probabilities of the distribution we seek to sample from. By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. """, """ Perhaps the most prominent application example is the Latent Dirichlet Allocation (LDA . \theta_{d,k} = {n^{(k)}_{d} + \alpha_{k} \over \sum_{k=1}^{K}n_{d}^{k} + \alpha_{k}} %PDF-1.3
%
\begin{equation}
;=hmm\&~H&eY$@p9g?\$YY"I%n2qU{N8
4)@GBe#JaQPnoW.S0fWLf%*)X{vQpB_m7G$~R >> XcfiGYGekXMH/5-)Vnx9vD I?](Lp"b>m+#nO&} Initialize t=0 state for Gibbs sampling. `,k[.MjK#cp:/r xWKs8W((KtLI&iSqx~ `_7a#?Iilo/[);rNbO,nUXQ;+zs+~! In fact, this is exactly the same as smoothed LDA described in Blei et al. 78 0 obj << endobj The MCMC algorithms aim to construct a Markov chain that has the target posterior distribution as its stationary dis-tribution. /Resources 17 0 R Data augmentation Probit Model The Tobit Model In this lecture we show how the Gibbs sampler can be used to t a variety of common microeconomic models involving the use of latent data. AppendixDhas details of LDA. xi (\(\xi\)) : In the case of a variable lenght document, the document length is determined by sampling from a Poisson distribution with an average length of \(\xi\). Bayesian Moment Matching for Latent Dirichlet Allocation Model: In this work, I have proposed a novel algorithm for Bayesian learning of topic models using moment matching called /Filter /FlateDecode To calculate our word distributions in each topic we will use Equation (6.11). Generative models for documents such as Latent Dirichlet Allocation (LDA) (Blei et al., 2003) are based upon the idea that latent variables exist which determine how words in documents might be gener-ated. Update $\mathbf{z}_d^{(t+1)}$ with a sample by probability. endobj Rasch Model and Metropolis within Gibbs. /Shading << /Sh << /ShadingType 2 /ColorSpace /DeviceRGB /Domain [0.0 100.00128] /Coords [0 0.0 0 100.00128] /Function << /FunctionType 3 /Domain [0.0 100.00128] /Functions [ << /FunctionType 2 /Domain [0.0 100.00128] /C0 [0 0 0] /C1 [0 0 0] /N 1 >> << /FunctionType 2 /Domain [0.0 100.00128] /C0 [0 0 0] /C1 [1 1 1] /N 1 >> << /FunctionType 2 /Domain [0.0 100.00128] /C0 [1 1 1] /C1 [1 1 1] /N 1 >> ] /Bounds [ 25.00032 75.00096] /Encode [0 1 0 1 0 1] >> /Extend [false false] >> >> This module allows both LDA model estimation from a training corpus and inference of topic distribution on new, unseen documents. /Resources 9 0 R To estimate the intracktable posterior distribution, Pritchard and Stephens (2000) suggested using Gibbs sampling. I can use the number of times each word was used for a given topic as the \(\overrightarrow{\beta}\) values. We demonstrate performance of our adaptive batch-size Gibbs sampler by comparing it against the collapsed Gibbs sampler for Bayesian Lasso, Dirichlet Process Mixture Models (DPMM) and Latent Dirichlet Allocation (LDA) graphical . Share Follow answered Jul 5, 2021 at 12:16 Silvia 176 6 In this paper, we address the issue of how different personalities interact in Twitter. 3. _conditional_prob() is the function that calculates $P(z_{dn}^i=1 | \mathbf{z}_{(-dn)},\mathbf{w})$ using the multiplicative equation above. /Matrix [1 0 0 1 0 0] /BBox [0 0 100 100] /Type /XObject Once we know z, we use the distribution of words in topic z, \(\phi_{z}\), to determine the word that is generated. \tag{6.1} B/p,HM1Dj+u40j,tv2DvR0@CxDp1P%l1K4W~KDH:Lzt~I{+\$*'f"O=@!z` s>,Un7Me+AQVyvyN]/8m=t3[y{RsgP9?~KH\$%:'Gae4VDS The intent of this section is not aimed at delving into different methods of parameter estimation for \(\alpha\) and \(\beta\), but to give a general understanding of how those values effect your model. w_i = index pointing to the raw word in the vocab, d_i = index that tells you which document i belongs to, z_i = index that tells you what the topic assignment is for i. % In particular, we review howdata augmentation[see, e.g., Tanner and Wong (1987), Chib (1992) and Albert and Chib (1993)] can be used to simplify the computations . + \alpha) \over B(n_{d,\neg i}\alpha)} Approaches that explicitly or implicitly model the distribution of inputs as well as outputs are known as generative models, because by sampling from them it is possible to generate synthetic data points in the input space (Bishop 2006). p(w,z,\theta,\phi|\alpha, B) = p(\phi|B)p(\theta|\alpha)p(z|\theta)p(w|\phi_{z}) Marginalizing another Dirichlet-multinomial $P(\mathbf{z},\theta)$ over $\theta$ yields, where $n_{di}$ is the number of times a word from document $d$ has been assigned to topic $i$. + \beta) \over B(\beta)} endstream Griffiths and Steyvers (2004), used a derivation of the Gibbs sampling algorithm for learning LDA models to analyze abstracts from PNAS by using Bayesian model selection to set the number of topics. The main contributions of our paper are as fol-lows: We propose LCTM that infers topics via document-level co-occurrence patterns of latent concepts , and derive a collapsed Gibbs sampler for approximate inference. D[E#a]H*;+now We also derive the non-parametric form of the model where interacting LDA mod-els are replaced with interacting HDP models. LDA using Gibbs sampling in R The setting Latent Dirichlet Allocation (LDA) is a text mining approach made popular by David Blei. endstream >> /Shading << /Sh << /ShadingType 3 /ColorSpace /DeviceRGB /Domain [0.0 50.00064] /Coords [50.00064 50.00064 0.0 50.00064 50.00064 50.00064] /Function << /FunctionType 3 /Domain [0.0 50.00064] /Functions [ << /FunctionType 2 /Domain [0.0 50.00064] /C0 [1 1 1] /C1 [1 1 1] /N 1 >> << /FunctionType 2 /Domain [0.0 50.00064] /C0 [1 1 1] /C1 [0 0 0] /N 1 >> << /FunctionType 2 /Domain [0.0 50.00064] /C0 [0 0 0] /C1 [0 0 0] /N 1 >> ] /Bounds [ 21.25026 25.00032] /Encode [0 1 0 1 0 1] >> /Extend [true false] >> >> Thanks for contributing an answer to Stack Overflow! /Filter /FlateDecode The idea is that each document in a corpus is made up by a words belonging to a fixed number of topics. In vector space, any corpus or collection of documents can be represented as a document-word matrix consisting of N documents by M words. So this time we will introduce documents with different topic distributions and length.The word distributions for each topic are still fixed. \end{aligned} Sample $x_n^{(t+1)}$ from $p(x_n|x_1^{(t+1)},\cdots,x_{n-1}^{(t+1)})$. In 2003, Blei, Ng and Jordan [4] presented the Latent Dirichlet Allocation (LDA) model and a Variational Expectation-Maximization algorithm for training the model. """ The tutorial begins with basic concepts that are necessary for understanding the underlying principles and notations often used in . This is were LDA for inference comes into play. &\propto p(z_{i}, z_{\neg i}, w | \alpha, \beta)\\ They proved that the extracted topics capture essential structure in the data, and are further compatible with the class designations provided by . 144 40
/FormType 1 These functions use a collapsed Gibbs sampler to fit three different models: latent Dirichlet allocation (LDA), the mixed-membership stochastic blockmodel (MMSB), and supervised LDA (sLDA). original LDA paper) and Gibbs Sampling (as we will use here). The interface follows conventions found in scikit-learn. (2003) which will be described in the next article. \tag{6.9} \prod_{k}{B(n_{k,.} How can this new ban on drag possibly be considered constitutional? 19 0 obj 0000002866 00000 n
CRq|ebU7=z0`!Yv}AvD<8au:z*Dy$ (]DD)7+(]{,6nw# N@*8N"1J/LT%`F#^uf)xU5J=Jf/@FB(8)uerx@Pr+uz&>cMc?c],pm# For complete derivations see (Heinrich 2008) and (Carpenter 2010). \end{equation} The authors rearranged the denominator using the chain rule, which allows you to express the joint probability using the conditional probabilities (you can derive them by looking at the graphical representation of LDA). """, """ 2.Sample ;2;2 p( ;2;2j ). /FormType 1 \end{equation} The only difference between this and (vanilla) LDA that I covered so far is that $\beta$ is considered a Dirichlet random variable here. Do roots of these polynomials approach the negative of the Euler-Mascheroni constant? By d-separation? /BBox [0 0 100 100] (NOTE: The derivation for LDA inference via Gibbs Sampling is taken from (Darling 2011), (Heinrich 2008) and (Steyvers and Griffiths 2007).).   In the last article, I explained LDA parameter inference using variational EM algorithm and implemented it from scratch. p(w,z|\alpha, \beta) &= $V$ is the total number of possible alleles in every loci. part of the development, we analytically derive closed form expressions for the decision criteria of interest and present computationally feasible im- . \]. /ProcSet [ /PDF ] Deriving Gibbs sampler for this model requires deriving an expression for the conditional distribution of every latent variable conditioned on all of the others. /Length 15 \prod_{k}{B(n_{k,.} > over the data and the model, whose stationary distribution converges to the posterior on distribution of . 1 Gibbs Sampling and LDA Lab Objective: Understand the asicb principles of implementing a Gibbs sampler. /Length 15 &={1\over B(\alpha)} \int \prod_{k}\theta_{d,k}^{n_{d,k} + \alpha k} \\ /Filter /FlateDecode \[ \begin{aligned} \]. ewLb>we/rcHxvqDJ+CG!w2lDx\De5Lar},-CKv%:}3m. # Setting them to 1 essentially means they won't do anthing, #update z_i according to the probabilities for each topic, # track phi - not essential for inference, # Topics assigned to documents get the original document, Inferring the posteriors in LDA through Gibbs sampling, Cognitive & Information Sciences at UC Merced. stream \end{equation} &={B(n_{d,.} Below is a paraphrase, in terms of familiar notation, of the detail of the Gibbs sampler that samples from posterior of LDA. << Gibbs sampling inference for LDA. The result is a Dirichlet distribution with the parameters comprised of the sum of the number of words assigned to each topic and the alpha value for each topic in the current document d. \[ % \[ 0000001484 00000 n
<< P(z_{dn}^i=1 | z_{(-dn)}, w) 0000015572 00000 n
special import gammaln def sample_index ( p ): """ Sample from the Multinomial distribution and return the sample index. 1. << 22 0 obj LDA is know as a generative model. What does this mean? machine learning 3.1 Gibbs Sampling 3.1.1 Theory Gibbs Sampling is one member of a family of algorithms from the Markov Chain Monte Carlo (MCMC) framework [9]. assign each word token $w_i$ a random topic $[1 \ldots T]$. In particular we are interested in estimating the probability of topic (z) for a given word (w) (and our prior assumptions, i.e. This is accomplished via the chain rule and the definition of conditional probability. You may notice \(p(z,w|\alpha, \beta)\) looks very similar to the definition of the generative process of LDA from the previous chapter (equation (5.1)). /Filter /FlateDecode Marginalizing the Dirichlet-multinomial distribution $P(\mathbf{w}, \beta | \mathbf{z})$ over $\beta$ from smoothed LDA, we get the posterior topic-word assignment probability, where $n_{ij}$ is the number of times word $j$ has been assigned to topic $i$, just as in the vanilla Gibbs sampler. 3 Gibbs, EM, and SEM on a Simple Example 0000004841 00000 n
For Gibbs Sampling the C++ code from Xuan-Hieu Phan and co-authors is used. The length of each document is determined by a Poisson distribution with an average document length of 10. p(A, B | C) = {p(A,B,C) \over p(C)} For a faster implementation of LDA (parallelized for multicore machines), see also gensim.models.ldamulticore. /Resources 7 0 R p(w,z|\alpha, \beta) &= \int \int p(z, w, \theta, \phi|\alpha, \beta)d\theta d\phi\\ Symmetry can be thought of as each topic having equal probability in each document for \(\alpha\) and each word having an equal probability in \(\beta\). The \(\overrightarrow{\alpha}\) values are our prior information about the topic mixtures for that document. lda implements latent Dirichlet allocation (LDA) using collapsed Gibbs sampling. Find centralized, trusted content and collaborate around the technologies you use most. endstream >> endobj &=\prod_{k}{B(n_{k,.} Replace initial word-topic assignment /Subtype /Form denom_doc = n_doc_word_count[cs_doc] + n_topics*alpha; p_new[tpc] = (num_term/denom_term) * (num_doc/denom_doc); p_sum = std::accumulate(p_new.begin(), p_new.end(), 0.0); // sample new topic based on the posterior distribution. endstream
endobj
182 0 obj
<>/Filter/FlateDecode/Index[22 122]/Length 27/Size 144/Type/XRef/W[1 1 1]>>stream
stream After running run_gibbs() with appropriately large n_gibbs, we get the counter variables n_iw, n_di from posterior, along with the assignment history assign where [:, :, t] values of it are word-topic assignment at sampling $t$-th iteration. To learn more, see our tips on writing great answers. >> \tag{6.10} /ProcSet [ /PDF ] (2003). I perform an LDA topic model in R on a collection of 200+ documents (65k words total). $\newcommand{\argmin}{\mathop{\mathrm{argmin}}\limits}$ 0000134214 00000 n
/Resources 11 0 R stream Topic modeling is a branch of unsupervised natural language processing which is used to represent a text document with the help of several topics, that can best explain the underlying information. /Filter /FlateDecode Sequence of samples comprises a Markov Chain. Not the answer you're looking for? 28 0 obj 4 . Before going through any derivations of how we infer the document topic distributions and the word distributions of each topic, I want to go over the process of inference more generally. p(, , z | w, , ) = p(, , z, w | , ) p(w | , ) The left side of Equation (6.1) defines the following: Styling contours by colour and by line thickness in QGIS. ndarray (M, N, N_GIBBS) in-place. In-Depth Analysis Evaluate Topic Models: Latent Dirichlet Allocation (LDA) A step-by-step guide to building interpretable topic models Preface:This article aims to provide consolidated information on the underlying topic and is not to be considered as the original work. \tag{6.4} Keywords: LDA, Spark, collapsed Gibbs sampling 1. p(\theta, \phi, z|w, \alpha, \beta) = {p(\theta, \phi, z, w|\alpha, \beta) \over p(w|\alpha, \beta)} \tag{6.6} Decrement count matrices $C^{WT}$ and $C^{DT}$ by one for current topic assignment. \int p(z|\theta)p(\theta|\alpha)d \theta &= \int \prod_{i}{\theta_{d_{i},z_{i}}{1\over B(\alpha)}}\prod_{k}\theta_{d,k}^{\alpha k}\theta_{d} \\ int vocab_length = n_topic_term_count.ncol(); double p_sum = 0,num_doc, denom_doc, denom_term, num_term; // change values outside of function to prevent confusion. Is it possible to create a concave light? viqW@JFF!"U# {\Gamma(n_{k,w} + \beta_{w}) Labeled LDA is a topic model that constrains Latent Dirichlet Allocation by defining a one-to-one correspondence between LDA's latent topics and user tags.