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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How to Use LSA Create Topics?

Just want to know the general process of creating document topics via LSA. For creating document clusters, I know first I should get SVD dimensions and then use k-means clustering on these SVD ...
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Example of Variational Inference for the LDA

there is one high-level example of the aplication of variational inference for the Latent Dirichlet Allocation? I am trying to understand the inference process from the LDA via variational inference ...
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How to pre-process data & tune hyperparameters of Doc2Vec?

I am using doc2vec for getting document similarity (unsupervised learning). I read that we need to shuffle the input matrix to doc2vec & reduce the learning rate for better performance. But the ...
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How can variational inference (for LDA) be explained in layman's terms?

I am learning probabilistic topic modeling, and I am studying latent Dirichlet allocation, specifically the inference process. I found many mathematical details, but I need a layman's explanation and/...
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Is the EM-algorithm the same thing that variational inference in LDA?

I am new in the probabilistic topic modeling, and I need to understand deeply the LDA process, I understand what want to do the inference process in LDA, and I understand too that there is 2 "types" ...
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30 views

Latent Dirichlet Allocation - basic question about generative process

After trying to understand Blei's 2009 paper "Topic Models" and reading several websites. This blog entry is the simplest explanation I could find. I still don't understand how LDA works. According ...
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22 views

Latent Dirichlet Allocation toolboxes

There are several tools for LDA. But I don't know which one is better and when? I wonder if anyone could guide me in choosing one toolbox. My priorities are ease-of-use and supporting various ...
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35 views

Latent Dirichlet Allocation for Twitter dataset

I'm trying to understand Latent Dirichlet Allocation (LDA) to apply on Twitter dataset. I've a dataset with 10k tweets and I've already splitted tweets in six groups. Now I'd extract topic from each ...
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19 views

Dynamic topic models, the proper structure for alpha

In the somewhat inscrutable papers by Blei et al., Dynamic Topic Models and Continuous-time Dynamic Topic Models, there's some ambiguity regarding the role of alpha. In Dynamic Topic Models, alpha is ...
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28 views

What are the hypothesis of using linear discriminant analysis (LDA)?

I want to use LDA for a classification problem. I have 14 independent variables including 11 continuous and 3 categorical variables. My response factors is binary {0, 1}. I have checked the ...
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28 views

How to calculate the perplexity of Twitter-LDA? [closed]

I am trying to evaluate different topic models. When it comes to Twitter-LDA, I am not sure how to evaluate the perplexity. I regard the background assignment as another topics. Thus there are totally ...
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57 views

Understanding LDA inference

It is said that the key inferential problem that needs to be solved to use LDA (latent Dirichlet allocation) is that of computing the posterior distribution $p(\theta,z | w, \alpha ,\beta)$. I know ...
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49 views

Word-topic matrix in Latent Dirichlet Allocation

In the latent Dirichlet allocation model described in Wikipedia, is $\beta$ the word-topic matrix? I understand that $\beta$ is the topic-word matrix and that $\beta_{ij}$ contains the probability ...
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31 views

Topic Modeling Dataset for Code Verification

I am trying to write up a Gibbs sampling Latent Dirichlet Allocation function for myself in R, and wanted to run it on a dataset where the true classification of ...
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88 views

How to check the convergence in the collapsed Gibbs sampling of LDA? [closed]

I am trying to implement the LDA model fit by collapsed Gibbs sampling by myself. I have go through this article. And there is a clear pseudo code (section 5.5), ...
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24 views

topic modeling for image retrieval

I'm interested in learning a topic model from a bag of visual words for image retrieval. I can compute V cluster centers (visual words) of SIFT descriptors at keypoints for each training image and fit,...
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Choosing words in a topic, which cut-off for LDA topics?

My question is what is a good cut-off threshold for LDA topics? I used the code in this blog post Topic modeling with latent Dirichlet allocation in Python In that code, the author shows the top 8 ...
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1answer
39 views

What metrics must i use in my data(unstructured) preprocessing research?

am currently working on preprocessing unstructured data (emails,logs,bug reports and irc chats). i wish to prove preprocessing improves the content quality. are there metrics available to prove this?...
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61 views

How high is “high” and how low is “low” in Latent Dirichlet Allocation Alpha and Eta hyperparameters? - LDA

In relation to this question and answer, the default value for the Python LDA for alpha is 0.1 and ...
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50 views

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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41 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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95 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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28 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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22 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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1answer
155 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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140 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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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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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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86 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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92 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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248 views

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 topic-...
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183 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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45 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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290 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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160 views

Topic modeling (LDA) gives different outputs

I am using Topic Modeling Tool which is based on Mallet and using latent dirichlet allocation (LDA). When I ran the tool multiple times, with the same input (a folder of 200-500 short text files), and ...
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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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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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227 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 semi-...
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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
30 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
467 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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245 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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146 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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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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49 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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56 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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41 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
396 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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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 ...