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25 views

In LDA, how to interpret the meaning of topics?

I am studying Latent Dirichlet Allocation (LDA) model, and I found some explanations around the web (for example here on Quora.com). In the link examples, I can clearly see which are the topics author ...
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0answers
13 views

plsa using maximum a posteriori

I have performed topic modeling by PLSA using maximum likelihood estimation. Now I need to perform using maximum a posteriori by using some prior distribution. The prior distribution consists of word ...
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1answer
66 views

Using topic words generated by LDA to represent a document

I want to do document classification by representing each document as a set of features. I know that there are many ways: BOW, TFIDF, ... I want to use Latent Dirichlet Allocation (LDA) to extract ...
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0answers
15 views

Can LSA find correlations between multiple words?

I need to find correlations between multiple terms (say, 3 or 4) in a single-term search index. I'm trying to figure out if LSA fits to the problem. Am I right that LSA is no more than a term-to-term ...
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0answers
33 views

Practical Implementation of Gibbs Sampling in Latent Diriclet Allocation

In the collapsed Gibbs sampling version of LDA, the posterior distribution of topic assignments for each word is sampled. From what I have read (e.g. ...
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1answer
132 views

Comparing topic distributions between corpora using Latent Dirichlet Allocation and R topicmodels or python gensim

So I am working on a problem where I want to extract a set of LDA topics from one corpus, and then compare the distribution of those topics in other corpora. So basically I want to lock-in the topics ...
2
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1answer
56 views

Basic Question about the Latent Dirichlet Allocation Generative Model

So Here is the LDA Generative Model The $\alpha$ and $\beta$ nodes represent the parameters for two Dirichlet distributions. The $\theta$ and $\phi$ nodes represent the parameters for two ...
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0answers
43 views

A question about Latent Dirichlet Allocation model

when I used LDA model in my project, the result topic terms vary with the random seed. how to solve this problem ? thanks
1
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1answer
116 views

understanding of effect of $\alpha$ in Dirichlet distribution

When reading the topic modeling tutorial written by Blei, KDD 2011 tutorial I was confused about a set of diagrams which aim to show the effect of $\alpha$ in Dirichlet distribution. For example, for ...
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1answer
32 views

Choosing the best set of keywords

I have a dataset of tweets collected using twitter streaming API on a particular topic (say 'football') using around 40 keywords. Now if I'm going to track the same topic (football) in future how do I ...
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0answers
52 views

How do I cluster documents using topic models?

Let us say I have a topic probability per document, for example: ...
0
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1answer
48 views

Using LDA in non-realtime twitter data

I try to understand user characterization from twitter data. How can I understand the user's interest from statuses? From my researches, LDA(Latent Dirichlet Allocation)suitable for topic extraction. ...
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1answer
106 views

General questions regarding text-classification

I'm new to Topic Models, Classification, etc… now I'm already a while doing a project and read a lot of research papers. My dataset consists out of short messages that are human-labeled. This is what ...
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1answer
112 views

Understanding Latent Dirichlet Allocation Inference

I'm reading the wikipedia page about how Latent Dirichlet Allocation assigns a topic distribution to a document after the model's been learnt (see this link). I'm very confused by this part of it: ...
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1answer
79 views

Sparse Additive Generative Models (SAGE) v/s LDA (Latent Dirichlet Allocation)

Intuitively, how are Sparse Additive Generate Models (SAGE) by Eisenstein, different from Multinomial Dirichlet distributions. I understood that in this model distributions are added in logarithmic ...
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1answer
112 views

How to generate new Topic for new documents?

what approach would help me generate new topics for new documents? I read this page in order to learn more about the effect of specifying keywords for the topics that we care about detecting in new ...
0
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1answer
104 views

Gensim Topic Modeling

I want to build a standard topic classifier. I was told gensim is the way to do it. I have difficulty training the gensim system. How do we provide a training data in a fast way. Some forum suggested ...
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2answers
82 views

Is it possible for a multinomial sample to be a single number?

I'm reading the Latent Dirichlet Allocation paper trying to understand it. However I got stuck at the very first part! When they sampled from a multinomial distribution and considered the result to be ...
2
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0answers
349 views

Gibbs sampling for LDA — does a small Dirichlet concentration parameter make a difference?

I'm using a Gibbs sampler for Latent Dirichlet allocation as described by Griffiths and Steyvers (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC387300/). The sampling of a new topic $j$ for word $i$ is ...
2
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1answer
130 views

At what point does LDA (Latent Dirichlet Allocation) not make sense to use?

I was wondering if anyone could provide some intuition about when the "documents" in LDA are too small for it to provide any benefit. For instance, I have seen papers where people have you used ...
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0answers
31 views

Sliding window EM algorithm for PLSA?

I'm wondering is there a version of sliding window EM? I'm working on an application involving a stream of text (tweets). We are thinking of using 1 day data as training set, and extract the topics ...
2
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0answers
66 views

Confusion related to correlation in topic models

I was reading this paper related to correlated topic models. However, I didn't understand this correlation figure. In the figure given below, I am confused about the level curves shown in the ...
0
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1answer
246 views

Topic models evaluation in Gensim

I've been experimenting with LDA topic modelling using Gensim. I couldn't seem to find any topic model evaluation facility in Gensim, which could report on the perplexity of a topic model on held-out ...
2
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0answers
27 views

Confusion related to intractability in topic models

I was reading this paper related to topic models. I am a bit confused why the marginal likelihood is not tractable and how converting the graphical model into the new one actually helps. First I ...
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0answers
32 views

Measure topic centrality

Does anyone know any metric that measure topic centrality or topic stickiness (I've seen someone used this term too). For example Cluster 1 $d_1$ = { a, b, c }, $d_2$ = { b, c, d } and Cluster 2 ...
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0answers
17 views

Heuristic to determine the number of latent classes [duplicate]

Do anyone know if in the field of topic modeling and latent class analysis, it exists some heuristic to determine a good number of latent classes?
2
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0answers
35 views

How to build event recommender based only on events' descriptions

I built a simple app that grabs events using eventbrite and meetup.com APIs and displays it based on your zip code. Now I'm trying to build a simple event recommender that will use events that you ...
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1answer
119 views

How to model the effect of “words occurring over time”?

As time goes on, some new words may occur. But standard Latent Dirichlet Allocation assume a fixed vocabulary. Is there some variation of the Latent Dirichlet Allocation model which can represent ...
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0answers
72 views

Iteration parameter in latent dirichlet allocation model

I want to find 24 topics in 800,000 documents by using LDA model, but how many iterations should I give? It is extremely slow when the parameter is large, like 3000.
0
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1answer
106 views

Implementing Latent Dirichlet Allocation - notation confusion

I am trying to implement LDA using the collapsed Gibbs sampler from http://www.uoguelph.ca/~wdarling/research/papers/TM.pdf the main algorithm is shown below I'm a bit confused about the notation ...
2
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0answers
42 views

How does word co-occurrence let two words from the same topic link together in LDA topic model?

The essence of LDA is word co-occurrence. I want to know why? and does word co-occurrence mean two words appear together in a certain document or just appear in the documents collection together? ...
2
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1answer
60 views

Topic modeling for grouped data

Is there a variation of Latent Dirichlet Allocation (LDA) for grouped data? As an example, let us consider corpus of all Yahoo Q&A (where for simplicity we consider a question lumped together with ...
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0answers
61 views

Posterior in latent Dirichlet analysis

I have a question regarding LDA (Latent Dirichlet Analysis) - what is the correct formulation of the posterior? In http://www.cs.princeton.edu/~blei/papers/Blei2011.pdf‎ it is $p(\beta_{1:K}, ...
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2answers
146 views

Is LSA and topic clustering easier in European languages similar to English?

I was watching a talk on latent semantic analysis and the speaker described experience applying LSA and REALLY messy data. He concluded that it demonstrated the difficulty of disambiguation of ...
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2answers
941 views

Topic stability in topic models

I am working on a project where I want to extract some information about the content of a series of open-ended essays. In this particular project, 148 people wrote essays about a hypothetical student ...
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0answers
283 views

Supervised Latent Dirichlet allocation (LDA) on a sentence level

I'm writing a bachelor thesis on formality classification of sentences in English via projecting the formal pronouns from foreign (e.g. French tu/vous) texts to English (you). It's not really ...
2
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1answer
94 views

Can LDA assign more than one topic for a word?

I have just started reading about Latent Dirichlet Allocation LDA and want to apply it to my project. May I know if LDA is able to assign a topic to more than one word? For example, Article A talks ...
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1answer
203 views

What are typical values to use for alpha and beta in Latent Dirichlet Allocation?

Specifically in the case where I don't know anything about the documents I'm working with. I'm looking a specific number or number range.
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0answers
123 views

Latent Semantic Analysis - Co-occurrence of words

Let $A[n\times m]$ represents the term-document matrix, where, $n$ is the number of terms and $m$ is the number of documents. This matrix can be composed into 3 matrices (SVD decomposition) such as, ...
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2answers
157 views

Understanding the derivation of an equation in LDA modeling

When reading the derivation of LDA models, I usually get the following equations. I do not quite understand the second step, where $p(\mathbf{z}_{-i},\mathbf{w}|\alpha,\beta)$ was removed. Is that ...
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1answer
153 views

Derivation of the posterior over topics in LDA

When studying the latent Dirichlet allocation, I am not very clear about some procedures in their deriving equations. Please refer to the attached figure, how to understand those two steps, marked as ...
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2answers
170 views

Is classifying documents according to their topic useful for any application? [closed]

Suppose that we have digitized document images; we pass this documents through OCR and we get text text documents, that we classify according to their topics in different classes such as payroll, ...
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1answer
148 views

Latent Dirichlet analysis document comparison

I have been using Latent Dirichlet Analysis for a while but I am a bit confounded as to it's practical ability to compare two documents. It is of course ideal for classification when you want to see ...
4
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1answer
321 views

Variational inference for nested Chinese restaurant process

I recently read paper by Chong Wang and David M. Blei "Variational Inference for the Nested Chinese Restaurant Process". And I couldn't understand the next part (from p.5): The variational update ...
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1answer
220 views

Nonparametric Topic Models

I am confused between what types of problems these three models capture, and their applications: Latent Dirichlet Allocation (LDA) Dirichlet Processes and Pitman-Yor processes Hierarchical Dirichlet ...
1
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1answer
452 views

Latent Dirichlet Allocation (LDA): What exactly is inferred?

I am working my way through LDA and I think I got they main idea of it. Please correct me if I am wrong. Given the Plate notation: The variables $\alpha$ and $\beta$ are Dirichlet distribution ...
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2answers
206 views

Topic Words Selection in Topic Modeling

I understand how generative model of topic modeling works; for each topic there is a distribution of words, and for each document there is a distribution of topics. Question is how words are ...
2
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1answer
207 views

LDA topic models for various and unknown domains

We have a large collection (1-2M) of documents of various domains (politics, design, programming, etc.). And lets assume that we don't know the exact number of domains. And our goal is to build ...
3
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1answer
507 views

How to optimize hyper-parameters in LDA?

After reading Hanna Wallach's paper Rethinking LDA: Why Priors Matter, I want to add hyper-parameter optimization to my own implementation of LDA. However, the paper doesn't given any details about ...
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1answer
630 views

Topic modeling, LDA and NMF

My objective is to implement a topic model for a large number of documents (20M or 30M). Let us assume that the number of topics is fixed at 50. I think implementing an LDA for the above problem ...