A topic model is a type of statistical model for discovering the abstract "topics" that occur in a collection of documents.

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About LDA model, I need a true expert to tell me that what is the real benefits of the Dirichlet prior?

Well,you know ,the only difference between pLSI and LDA is that the latter has a Dirichlet prior,thus the number of model parameters do not increase with the size of corpus,and this avoid the ...
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Neural network to tag multiple textual topics in a single document

I want to use a neural network to do some topic analysis in a textual corpus. I have used neural networks before where there is a clear decision boundary between the category to which some observation ...
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Can topic model be used to classify documents ?

Is dynamic topic model can be used to label data ? I divided a unlabeled dataset into training and testing sets. Is it rational to label the training data with dynamic topic model (I want to take time ...
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How Perplexity measured effectiveness of topic modeling?

I am confusing about Perplexity, who can explain me How Perplexity measured effectiveness of Topic modeling by an example? (perceptible and easy to understand) for example suppose we have phi and ...
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topic similarity semantic PMI between two words wikipedia

I am trying to compute pointwise mutual information (PMI) using wikipedia as data source. Given two words, PMI defines the relation between two words. The formula is as below. ...
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Data set for document topic discovery

I have a bunch of documents and would like to to assign one (or more) topics to each document. The topics can be quite wide ranging from political topics to sports, etc. I think the best way is to ...
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Diagnostics for Gibbs sampler in R package topicmodels

I'm experimenting with topic models for my master thesis. I have a dataset consisting of about 300 variables representing words and 900 cases. It's not in document form, because I pre-selected certain ...
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79 views

Interpreting the result of topic modelling using Latent Dirichlet Allocation

I have an unpublished corpus consisting of 135 texts representing one year each. I do topic modelling (using Mallet) and then inspect the topic distribution over time. The overall picture looks good: ...
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Latent Dirichlet Allocation example in PyMC doesn't seem to give correct results

I've been working on the PyMC example for Latent Dirichlet Allocation (LDA) in this other post. The problem is that it doesn't seem to actually find the correct topics. For the code below my ...
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Does one need to adjust for document length (in terms of pages) in topic modeling?

I am thinking about whether one needs to normalize or weight a topic model by document length (page length)? I am estimating a topic model using social science (JSTOR) articles, where they vary in ...
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Running Latent Dirichlet Allocation (LDA) on word counts

I have difficulties understanding the VB implementation lda-c. In particular, the method expects as input a bag-of-words representation of documents, where distinct words appearing in a document are ...
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Evaluation of LDA

I am looking for a C++/Java implementation for computing the perplexity of held-out document in Latent Dirichlet allocation. Can anybody suggest useful links?
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Implement a Bigram Latent Dirichlet Allocation (LDA) for Topic Modeling

I'm trying to implement Latent Dirichlet Allocation (LDA) on a bigram language model. This is described in Topic Modeling: Beyond Bag-of-Words by Hanna Wallach et al. I'm trying to easily implement ...
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advice regarding which ant clustering algorithm to choose

I am working on this project in which I am going to take a small corpora of input text consisting of works of literature from different genre. After extracting a set of features, I wish to perform ...
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52 views

EM to Variational EM in LDA

Why exactly, when learning hidden variables distribution in LDA(Latent Dirichlet Allocation), one cannot use to the EM (Expectation Maximization) algorithm and have to resort to a variationnal EM ...
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51 views

Incorporating metadata to a supervised Topic Model

I have texts and their metadata and a response variable (how many times the text has been read). I'm interesting in finding out how the latent topics in the set of texts are related to the popularity ...
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146 views

vowpal wabbit LDA

I am trying to use vowpal wabbit to do LDA on a corpus. I am running into a few issues regarding the output. To test it, I was using a file with just 3 lines (3 documents as per the VW input format) ...
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Is this a correct way to do document classification using topic modeling?

I am using LDA to extract topics. I want to do topic modelling and use the topics as features to do document classification. I am proposing the below approaches using scikit-learn. I want to know ...
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What does it mean to say that “a topic is a distribution on words”?

I was taking a machine learning course and they say the following two phrases that confuse me: each document is a distribution on topics. and each topic is a distribution on words. I was ...
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Can a labeled LDA (Latent Dirichlet Allocation) dataset have just one label per document?

I understand that in labeled LDA, every document should be associated with a set of labels which are known as tagged topics for the respective document. My question is whether a document can be ...
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Are there Relational Topic Model implementations available anywhere other than {lda} Gibbs Sampler?

Reading the help of CrossValidated about wether or not this question would belong here and StackOverflow it says "if it needs statistical expertise to understand or answer, ask it here". Since this is ...
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Latent Semantic Analysis with automatically discovered priors - gensim

I have around 20,000 English words in a set, mostly nouns. Something like {berry,bloom,buddy,comic,front,mind,charlie,consultants,destination,enterprise,experts,lady,stores,weight,arms,autism,balloon} ...
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Hierarchical Dirichlet Processes in topic modeling

I think I understand the main ideas of hierarchical dirichlet processes, but I don't understand the specifics of its application in topic modeling. Basically, the idea is that we have the following ...
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(LDA) Topic Modeling: eliminate Junktopics through normalization

Question: Is it reasonable to normalize topics to eliminate junk-topics and get a better distinction of document-relations? I used the MALLET-LDA Java-library to estimate a ParallelTopicModel with ...
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Correlated topic models in the lda R package

Can someone either explain to me or link to documentation which explains how to use correlated topic models in the lda package for R, the official documentation ...
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Topic Model inference for subselection

Question: Does the inference of a trained topic model (LDA) used on a subselection of a text corpus result in more accurate document-document-relations? I used the MALLET-LDA Java-library to estimate ...
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Topic models (LDA), word cooccurances in documents?

I have read on papers that Latent Dirichlet Allocation (LDA) works by identifying word cooccurances in documents. What is confusing me is since LDA uses bag-of-words approach for document ...
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Gibbs sampling version for estimating the Dynamic Topic Model (DTM)?

The paper of Blei et Lafferty published at ICML'06 implements a (quite complicated) variational inference (VI) technique for estimating the parameters of the Dynamic Topic Model, see: ...
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A single document as input to LDA?

We use topic modelling usually on a collection of documents - which makes the input. But what if I only have a single document where I want to see the underlying topics in it? I have heard that you ...
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Descriptive clustering of papers

Given a set of PubMed abstracts or keywords derived from MeSH terms, I would like to know how many and what topics are among them in order to write a paper review. Other information such as the number ...
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Automatically generated topics‏

What is difference between the extention of LDA (Dependency -LDA) in : http://psiexp.ss.uci.edu/research/papers/RubinEtAl_2011_MLJ_SpecialIssue_2ndResubmission_V14p1.pdf and the CoL Model(Correlated ...
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Topics in Latent Dirichlet allocation

I understand that an Latent Dirichlet allocation models each document as a mixture of topics where a topic is a distribution over words. What is not clear to me whether I need to manually specify ...
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Converting numbers in linear space to binary using sigmoid in R

I'm running a binary prediction using a supervised topic modeling package in R (lda package, using slda.predict function). The ...
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How do you estimate $\alpha$ parameter of a latent dirichlet allocation model?

Blei has shown that it is possible to estimate $\alpha$ in a LDA model, but I have yet to find a library (any library; C, C++, Java, ...) to do so. Usually, implementations (including Blei's) treat ...
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Can dummy variables be used to represent space in latent Dirichlet allocation?

Can dummy variables be used to represent space in latent Dirichlet allocation? I have a set of geocoded textual documents. I would like to use LDA to generate a topic model for the documents. ...
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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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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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620 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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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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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 ...
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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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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
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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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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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How do I cluster documents using topic models?

Let us say I have a topic probability per document, for example: ...
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Using LDA in non-realtime twitter data

I'm trying to understand user characterization from twitter data. How can I infer a user's interests from their status updates? LDA (Latent Dirichlet Allocation) seems to be a suitable approach to ...
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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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200 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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213 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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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 ...