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25 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 ...
0
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0answers
34 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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0answers
26 views

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 ...
4
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1answer
107 views

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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0answers
20 views

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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0answers
22 views

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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0answers
36 views

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} ...
0
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1answer
56 views

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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0answers
17 views

(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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0answers
49 views

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 ...
1
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0answers
17 views

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 ...
1
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0answers
27 views

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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0answers
33 views

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: ...
0
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1answer
57 views

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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0answers
27 views

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 ...
0
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0answers
13 views

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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1answer
39 views

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 ...
1
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0answers
80 views

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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1answer
53 views

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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1answer
41 views

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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0answers
68 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
19 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 ...
1
vote
1answer
269 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
21 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
49 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. ...
1
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1answer
313 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
95 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
47 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
190 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 ...
0
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1answer
39 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
95 views

How do I cluster documents using topic models?

Let us say I have a topic probability per document, for example: ...
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3answers
99 views

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 ...
1
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1answer
141 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 ...
0
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1answer
168 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: ...
0
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1answer
146 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
128 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 ...
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2answers
88 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
470 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
173 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 ...
2
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0answers
75 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 ...
1
vote
1answer
385 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
30 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 ...
2
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0answers
44 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 ...
5
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1answer
120 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 ...
0
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0answers
122 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.
1
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2answers
141 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 ...
3
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0answers
52 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
votes
1answer
73 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 ...
0
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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}, ...
3
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2answers
161 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 ...