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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 does topic coherence score in LDA intuitively makes sense ?

referring to: http://qpleple.com/topic-coherence-to-evaluate-topic-models/ In order to decide the optimum number of topics to be extracted using LDA, topic coherence score is always used to measure ...
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17 views

Hierarchical Dirichlet Process - What is the use of the base distribution H?

I am trying to get a better grasp on Hierarchical Dirichlet Process. My question is: Is the base distribution H the substitute of the exact number of topics in comparison to LDA?
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60 views

Assess the dependence of LDA on the random seed

New to latent Dirichlet Allocation (LDA), I would like to be sure that my output (in the first the step, the word-per-topic probabilities) depends on the input merely, and is (somewhat) stable ...
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37 views

Predict document topic with LDA using only document-topic probabilities

I did a LDA topic analysis to a corpus of N documents. Due to few unlucky events all word-topic -probability distribution matrices was lost and I am left with only the document-topic probabilities $P(...
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19 views

LDA corpus stability [closed]

I am working on an LDA to find out topics from a corpus. Everything works fine, but when I add some lines in my corpus, the topics change a lot. This should not happen because the added topics ...
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1answer
83 views

Can I use word2vec vectors as input features to NMF or LDA?

I'm trying to do some topic modelling on my corpus and I want to use Word2Vec vectors as an input to my NMF and LDA models. How do I do this? Is it even possible?
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34 views

nmf in scipy returns components with all zero weights

I'm trying to understand whether this behavior is a bug or a feature. Essentially, I have a dataset of ten thousand short pieces of text. I have used the CountVectorizer function to turn this into a ...
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36 views

how is the vector representation of a out of sample document calculated on gensim's LDA implementation? [closed]

I am not sure of how gensim's LDA implementation obtains the vector representation of an unseen document. ...
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12 views

Matrix of probabilities of unsupervised labeling vs annotations [closed]

Given a list of texts, annotated by topic (each text can have multiple topics), and the document-topic matrix output of an LDA (Latent Dirichlet Allocation), unsupervised model. For example: ...
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15 views

How to evaluate the result of topic modeling such that time matters

I have run different topic modeling approaches on my data (clinical data related to Cognitive impairment diseases). we are going to process what is important that makes it develop to a harsher disease....
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19 views

Why the term-topic matrix of Latent Dirichlet Allocation strucutre changes when few sentences added to corpus

You would expect that the term-topic matrix or Phi, would have slight change in the strucutre when you add few words to the corpus analyzed by LDA, but once few words are added, it changes the matrix ...
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12 views

Use topic modeling to determine the topic popularity

I have a collection of documents. My end goal is to determine the popularity of the topic discussed by each document. In other words, whether (or to what extent) one document is discussing a popular ...
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42 views

Using GARCH and LDA

I'm working on a regression model using Latent Dirichlet Allocation (LDA). Using daily news data, I'm using a GARCH-model to see if different topics found using LDA indeed are significant in the ...
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17 views

Classifying trending and not trending topics using LDA

How can we differentiate the topics obtained from LDA as trending and not trending topics? What are the factors we can consider for this classification?
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1answer
53 views

How Specifically do Sampling methods help in training Machine learning models?

I get the gist of sampling methods in probability. These algorithms were developed while building the Atom Bomb to estimate some distribution. The idea was just to try a simulation and note the ...
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1answer
131 views

How to improve performance for LDA?

I am running LDA on health-related data. Specifically I have ~500 documents that contain interviews that last around 5-7 pages. Other than that, I cannot really go into the details of the data due to ...
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182 views

LDA TopicModel Alpha Parameter (Gensim)

I fit a topic model on python with 8 topics with alpha set to auto. I am trying to determine what my alpha value is for the model. the ldamodel.alpha command in gensim outputs an array, with 8 ...
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27 views

Help with variational Inference for Hierarchical Dirichlet Process

I am going over this paper by Chong Wang et. al. titled "Online Variational Inference for the Hierarchical Dirichlet Process" and I am struggling with the derivation of equation (17): $\varphi_{jtk} \...
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22 views

how to classify text that belongs to multiple (possibly missing) categories?

I have a dataframe that is very similar to the classic Reuters News topic classification dataset, and I am interested in ...
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14 views

Topic assignment in a topic document matrix. How to be robust?

I have a Document-Topic matrix whose scores were produced by multiplying a Topic-Term matrix produced by non-negative matrix factorization on a Term-Term matrix and the original Document-Term matrix ...
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33 views

Is the Latent Dirichlet Allocation topic posterior multimodal?

In fitting the Latent Dirichlet Allocation with collapsed Gibbs sampling one builds a sampled approximation to the topic posterior distribution, $P(z|w)$ and use that to calculate the topic and word ...
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33 views

Adjusting for selection bias in a structural topic model

I want to build a structural topic model that adjusts for selection bias into the sample of entities about whom documents are written. I wanted to use the stm R ...
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1answer
47 views

LDA topic modelling improvement

I am working on an LDA model to identify the topic of ~100,000 online courses based on their course descriptions and titles. In the further process, I would like to use these topics to cluster these ...
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19 views

What is the difference between Latent Dirichlet Oversampling,Distributional Random Oversampling and DECOM?

I was reading this paper on oversampling for handling class-imbalance in multiclass text classification. In this paper, the author is describing a topic distributional model for oversampling. He is ...
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21 views

Is there a dynamic hierarchical topic modelling system?

my name is Markus and I want to develop a dynamic hierarchical topic clustering system for written documents. The requirements are: no fixed hierarchy -> model should built up hierarchy by itself ...
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172 views

Similarity between documents in LDA “word” vectors space?

Using LDA we can convert a bag-of-words document into a vector of topics $\theta$ to incorporate statistical relations between words, then document similarity can be measured in the "topic" space by ...
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1answer
34 views

How to use hierarchical Dirichlet process to predict new word's probability

Assuming that I have already obtained document's topic distribution and topic-word distribution, how to predict the probability of one document will contain some word w_n?
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1answer
51 views

Does it make sense using Machine Learning techniques on a sparse features matrix?

I am trying to predict the sentiment (neg/neutral/pos) of a given text. To do so I use a LDA model (Latent Dirichlet Allocation) that is a topic discovery model. The LDA model works as follows: given ...
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2answers
97 views

latent dirichlet allocation: complexity and implementation details

I was confused by how LDA (by the original variational inference) can be implemented in a way such that the number of operations for each document $j$ is $\mathcal{O}(N_j~K)$, where $N_j$ is the ...
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2answers
54 views

Does Latent Dirchlet Allocation Work with Bag Of Words Model?

I was watching a tutorial on topic modeling and no-where they talk if the number in the bag of words model is significant. i.e. they only care whether word "a" belongs in the document or not, how ...
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1k views

How does LDA (Latent Dirichlet Allocation) assign a topic-distribution to a new document?

I am new to topic modeling and read about LDA and NMF (Non-negative Matrix Factorization). I understand the training process work. Let's say I have 100 documents and I want to train an LDA for these ...
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1answer
2k views

Inferring the number of topics for gensim's LDA - perplexity, CM, AIC, and BIC

I am confused as to how to interpret the LDA's perplexity fluctuations with different numbers of topics, in the endeavour of determining the best number of topics. Additionally, I would like to know ...
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51 views

Interpreting Sign of Loadings in Truncated SVD

I'm performing Truncated SVD on a word co-occurrence matrix for topic extraction. I'm using the scikit-learn implementation. I have read conflicting ideas on how to interpret the sign of the ...
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83 views

R STM package: Number of documents influence topic prevalence?

Would the number of documents under a covariate influence the topic prevalence? For example, I have a bunch of documents spread across a timeline. Now I want to explore the relations between the topic ...
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47 views

Algorithms utilising word confidence in Latent Dirichlet Allocation

I trying to do topic mining on automatically transcribed texts using latent dirichlet allocation. The limiting feature I am encountering in this is the accuracy of the transcription where each word ...
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1answer
99 views

From LDA output to W2V to K-means?

I'm working on review data where each review is labeled as positive or negative. My aim is to find topics in those reviews which are perceived either positive or negative. Therefore I performed a LDA ...
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52 views

How to filter out noise when topic modeling

I know the question is quite vague but allow me to set the stage in more detail: Currently, I am working with inputs that contain a short description (tweet size) and long description (paragraph) and ...
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1answer
88 views

Gibbs sampling in Hierarchically Supervised LDA (HSLDA)

TL;DR In the HSLDA paper by Perotte et al, the posterior conditional distribution of $z_{d,n}$ for Gibbs Sampling is specified as: \begin{equation*} p(z_{d,n}=k| \mathbf{z}_{-d,n}, \mathbf{a}, \...
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52 views

Latent Dirichlet Allocation with Very Specific Word and Topic Collection from Documentation

Let's say I work in a company that does something and most of the time the employees are confused about something and ask them using some company-made apps. The thing is, most of the time, new ...
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1answer
509 views

Beta distribution parameter estimation: method of moments

In a paper: Topics over time, method of moments was applied to estimate $\alpha$ and $\beta$ for a Beta distribution. My question is that how $\alpha$ or $\beta$ should be calculated if there are no ...
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217 views

State-of-the-art: unsupervised learning for patterns in text

My gut tells me tried-and-true approaches like k-means and Latent Dirichlet Allocation may no longer be state-of-the-art approaches for unsupervised learning with text data, what with models like ...
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1answer
156 views

Where does the exponential time complexity in LDA's posterior of topics arise?

In Finding scientific topics (PNAS 2004) the authors derive the (marginalized) posterior distribution of topic assignments given the observed word and arrive at equation (4). Then, immediately after, ...
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72 views

Should i stem my text for small corpra topic modelling

I found an interesting question around topic modelling for small corpora: Topic models for short documents This got me to wondering if i am doing analysis on topic modelling on small amounts of text ...
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32 views

How does using a correlated topic model effect the distribution of topics?

Correlated Topic Models are a great advance on the original topic model - see Blei and Lafferty 2007 for more info. My question is this - how does a Correlated Topic Model impact the overall ...
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2answers
436 views

Topic detection for a sentence or an article using Machine learning

Besides LDA (Latent Dirichlet allocation), are there other ways or methods to detect a topic or category from a sentence? For example, all the categories or tags from news websites can be used to ...
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1answer
255 views

Some topics with all-equal weights when using LatentDirichletAllocation from scikit-learn

I want to employ Latent Dirichlet Allocation (LDA) for topic modeling and I'm trying out the implementation from scikit-learn for that. Running the example (which uses messages from newsgroups as ...
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1answer
561 views

How can you apply sentiment analysis on topic modelling topics?

I need to perform sentiment analysis on topic modelling. I have used LDA with grid search search to identify the topic. The next step would be to perform sentiment analysis on it. How should I go ...
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1answer
251 views

Latent Dirichlet Allocation limit in number of topics?

In most of the topic modeling prior literature with LDA, the number of topics is in the range of 50-300. In big data scenarios, we may need a large number of topics, say 10k or 100k.For example, if ...
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44 views

Using Latent Dirichlet Allocation on Contrived Data

This is a cross-post of a post that was originally submitted to Math.Overflow I am doing a project that seems like it might be susceptible to Latent Dirichlet Allocation. However, my data is highly ...
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In LDA,why same document content could assign different topic?

I'm running LDA topic modelling on my corpus. In this corpus, there are numbers of documents with exactly the same content, and it is just one word. I understand each document could assign to ...