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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Calculate similarity of probability vector and TFIDF vector

I want to compute the similarity between documents of two groups of documents. All documents have the same term vector of length 100, but documents in group A are probabilities, whereas documents in ...
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21 views

which classifier to choose for probability histogram-like features

I have a populations of 500 elements. Each element is represented by a 10 dimension feature vector which sum of element is equal to 1 (you can think about it as a histogram of probabilities). In ...
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42 views

Text Classification for (Short) Open-Ended Survey Responses?

I am new to text mining/classification, and really want to learn more from this community. My data are open-ended survey responses (n=about 6,000) in which the respondent described what happened at ...
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25 views

Why sampling for inference of a new document in LDA?

Given a standard LDA model with few 1000 topics and few millions of documents, trained with Mallet / collapsed Gibbs sampler: When inferring a new document: Why not just skip sampling and simply use ...
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23 views

Fit statistics for multiple penalised multinomial models using cv.gmnet

I am using the R package glmnet for a prediction problem. Specifically, I am using a penalised multinomial model to classify a bunch of texts into some predefined ...
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14 views

Database for topic modeling [closed]

I would like to implement the (LDA) topic modeling scheme of the following paper by C. Wang and D. Blei: Collaborative topic modeling for recommending scientific articles (2011) Question For this ...
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31 views

How to determine the number of iterations for Latent Dirichlet Allocation

I am performing Latent Dirichlet Allocation for 240 test documents (trained model with 3361 documents). I am using 150 iterations and 120 burn in iterations. Is there a specific way to determine ...
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1answer
69 views

Calculating precision and recall for LDA

As I understand from Latent Dirichlet Allocation (LDA) algorithm, it produces two matrices- one for document-topic assignments, and one for topic-terms assignments. LDA is unsupervised machine ...
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27 views

What method for grouping documents by topic

I have a corpus of publications in CS divided by year. What I'd like to discover from it is The subject (only one) of each article ( for example testing, software engineering, networking, ...
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33 views

How does one determine which variables can be collapsed in Gibbs Sampling?

I am going through the derivation for Gibbs Sampling update equations for LDA. The claim is that $\theta_{d}$ (document specific topic distribution) and $\phi_k$ the topic-word distribution can be ...
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59 views

When is it ok to *not* use a held-out set for topic model evaluation?

Typically, I see people calculate the perplexity/likelihood of a topic model against a held out set of documents, but is this always necessary/appropriate? I'm thinking in particular of a use case in ...
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37 views

Calculating LDA perplexity when using TFIDF

Following this explanation, the perplexity of an LDA topic model is equal to: $Perplexity = \exp(-\frac{L(w)}{N})$ where $L(w)$ is the log-likelihood of the model, and $N$ is the number of tokens. ...
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59 views
+50

Calculation of word-word similarity in an LDA topic model

I'm using Graphlab's LDA functionality, and I get two matrices as the result: The document topic matrix (i.e. P(Topic|Document) for each document) and the the ...
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66 views

Topic modeling (LDA) and n grams

I am new to text mining.... reading stuff about it and setting up some KNIME workflows and Python tooling. I'd like to analyze customer feedback from surveys to derive topics from it without going ...
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29 views

I am getting a very low theta value (~9%) when performing LDA in R, is this typical?

I have performed LDA using the topicmodels package in R. When I was inspecting my theta values for each document in the corpus (~450,000 documents), I noticed that the average max theta, or in other ...
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1answer
157 views

When to use LDA over GMM for clustering?

I have a dataset containing user activity with 168 dimensions, where I want to extract clusters using unsupervised learning. It is not obvious to me whether to use a topic modelling approach in Latent ...
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2answers
100 views

Topic modeling (LDA) gives different outputs

I am using Topic Modeling Tool which is based on Mallet and using LDA. When I ran the tool multiple times, with the same input (a folder of 200-500 short text files), and set Topics = 10 with the ...
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27 views

LDA or pLSA for short documents?

I'd like to classify short documents, from a predefined set of words. What algorithm would you suggest, LDA or pLSA ? My use case I have a list of users, and for each user a list of the pages she ...
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3 views

M/E_k/1 queueing model

I need practical application for M/E_2/1 queueing model and also need application for fluid model and fluid queue driven by M/E_k/1 queueing model.
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148 views

Can doc2vec or word2vec used for (un/semi - supervised) topic modeling?

Sampling methods used in these methods are different from simple bag of words. They are usually suitable for supervised learning. Can one or both of these models be extended for unsupervised or ...
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69 views

Latent Dirichlet Allocation yields different posterior distribution than simple Bayesian model

Method A: out of the box LDA I am using a package to run LDA on a sample of size m with n words in the vocabulary. The end ...
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1answer
24 views

predict upvotes a review would get in a certain time period after publishing--yelp review

I've encountered this question online. How to design an algorithm to predict how many upvotes a review would get in a certain time period after publishing. Let's say the review comes from Yelp. So the ...
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1answer
259 views

How to optimize the number of topics using R Mallet

I would like to select the optimal number of LDA topics using R's mallet library. I know that there are several ways to do this using other implementations of LDA ...
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1answer
122 views

Benefits of clustering algorithms and Latent Dirichlet Allocation / topic models for finding clusters of words / topics in text

I am interested in finding clusters of words / topics in text. I am trying to learn more about potential approaches. The Wikipedia page on document clustering seems to provide a helpful overview ...
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66 views

Limitation of LDA (latent dirichlet allocation)

I'd like to get a list of limitations of LDA. I know that LDA does not work for short document set like a set of tweets very well. Are there such known limitations of LDA? Some reference including a ...
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1answer
44 views

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

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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1answer
42 views

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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1answer
50 views

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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34 views

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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1answer
243 views

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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3answers
93 views

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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27 views

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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1answer
184 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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125 views

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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90 views

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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1answer
67 views

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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2answers
123 views

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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573 views

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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38 views

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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162 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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76 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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1answer
276 views

Vowpal wabbit LDA

I am trying to use vowpal wabbit to do Latent Dirichlet Analysis (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 ...
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395 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 ...
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1answer
242 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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153 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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93 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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1answer
158 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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53 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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23 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 ...
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84 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 ...