Questions tagged [topic-models]

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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Topic modelling on only 24 documents gives the same “topic” for any K

Description: I have 24 documents, each one of around 2.5K tokens. They are public speeches. My text preprocessing pipeline is a generic one, including punctuation removal, expansion of English ...
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10 views

Hierarchical LDA (HLDA) topics hierarchy tree level?

What are the meanings of Hlda topics tree hierarchies? Tree has different height level like level 1, 2 and 3. What are the meanings of these level? Which level shows accurate results?
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LDA alpha equivalent in structural topic model

I'm using an implementation of the structural topic model (stm), written in R using the stm package. I want to reduce the number of topics that are prevalent in ...
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24 views

Considering the Sentiment rather than the topic in lDA model

I have a question about the lDA model, what if I consider sentiment rather than topic in this model? In this way, will the model classify the documents by sentiment? I have tested it, but it not work.
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21 views

Why is my correlation matrix dropping that many NA?

I am trying to build a correlation matrix among documents per topic on a Latent Dirichlet Allocation model by text2vec, getting a doc_topic_distr matrix like below, with only first 5 documents, it's a ...
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How to intuitively interpret of topic distributions/coefficients in topic modeling

I know topic modeling such as LDA can provide the distribution of topics in documents, and the distribution of topics represent the important of that topic. However, I'm having a hard time intuitively ...
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Does it make sense to apply Latent Dirichlet Allocantion on topic outcomes from the model?

What I mean is that if it makes sense to apply LDA twice, the first time as it is supossed to and the second apllying the model to each topic outcome from the first application? If it is possible, ...
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105 views

what does negative coherence score means?

I am working on topic modeling and when I tried to see the coherence of the topics all are negative. Does anyone know what could be the interpretation behind negative score? This is the example of ...
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28 views

Topic Modeling: How many documents “belong” to a topic?

A frequent question that arises when I present results of topic modeling to novices is: "How many documents belong to topic x?". As topic modeling is probabilistic, I hesitate giving absolute ...
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47 views

posterior predictive distribution for latent dirichlet allocation model

I want to obtain posterior predictive distribution on the LDA model, actually, I want to predict n next sample ( words in this model). can anyone help me? I attach the LDA model here, and it is ...
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9 views

Weighing documents in Topic Modeling, preset some topic/word probabilities

The Latent Dirichlet Allocation (LDA) results on my dataset are neither stable nor very interpretable, so I am looking for ways to "help" the LDA. I would like to pursue two ideas: 1) My documents ...
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Joining many topic models. How to join similar topics?

I'm doing topic discovery on a large corpus of small texts. I'm using many topic models, because not having a ground truth, each model covers a different semantic dimension. So now I ended with more ...
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150 views

LDA/NMF Topic Modeling vs Topic Modeling using “skip gram” approach

I am having a little friendly debate with my coworker on how to properly/optimally do topic modeling. I am just using the regular traditional nmf/lda approach and he decided to do it using "skip grams"...
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27 views

Can NMF assign probabilities to the topics it outputs?

It's my understanding that only LDA can assign probabilities to words within each topic that it discovers since it's a probabilistic graphical model politicians 0.05 united states 0.10 obama 0.20 ...
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25 views

How to interpret the difference in topic proportion (theta / document-topic-probability) of two groups?

I am comparing the difference in mean topic proportion (theta / document-topic-probability) of two groups. How can I interpret whether the differences are small or large, as topic proportions depend ...
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Stop words selection in topic modelling

I am doing topic modelling in below corpus having 5 documents only with pre defined two topics initially, bank and restaurant. First two documents are about bank and 3rd and 4th documents are about ...
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76 views

Custom topic priors in LDA

I've been working with LDA (Latent Dirichlet Allocation topic model) for a while now and I believe I have an intermediate understanding of it. The unsupervised nature of LDA is one of its big ...
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18 views

Metrics used in evaluation of topic models

I know that perplexity requires a held out corpus. I am new to coherence measures. What are intrinsic and extrinsic coherence measures? Which one of them require an external corpus like Wikipedia ...
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21 views

Evaluating topics generated by topic models

I am working on topic modelling of Amazon reviews and descriptions. I read that the coherence measure is better because it is correlated with human topic interpretation. The online resources explain ...
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how to perform statistical analysis on topics produced by topic models?

I have a large corpus of over 1.2 million text documents. After applying LDA to a small sample of the corpus, I have determined the number of topics be 34 using topic coherence score. Here, some ...
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23 views

Classify documents using a set of known vocabularies

I have a bunch of documents that I want to classify which ones talk about soccer (unsupervised learning, I do not want to manually label the documents). One way I am thinking about is to go online ...
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17 views

Biased viterbi training result

I try to use GMM-HMM model to infer the topic of sentences in a short paragraph. While instead of using normal Baum-Welch optimization, I use viterbi training as follows. I use average of word ...
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159 views

LDA detect new emerging topics

Thanks for stopping by. I have a directional question - I've built a Latent Dirichlet Allocation using Gensims Mallet wrapper. I trained the model once on OldDataSet.csv and measured coherence. I have ...
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Interpretation of NLP pipeline for topic discovery using gaussian mixture model clustering

I built a pipeline that does the following to discover topics out of a (very big: 50k docs per ~350 terms) Term Document Matrix: Compute the TfIdf score for each Term x Document pair; Rescale each ...
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1k views

What is the formula for c_v coherence?

I've recently been playing around with Gensim LDAModel. I use coherence to evaluate the results. Gensim offers a few coherence measures. This includes c_v and ...
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74 views

Gibbs sampler: how can thinning equal to the number of iterations work?

I fit an LDA topic model, using the R package topicmodels. No hiccups and everything runs smoothly, my question here is conceptual. When controlling the Gibbs sampler, the default value (in the ...
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179 views

Choosing the number of topics in topic modeling with multiple “elbows” in the coherence plot

When plotting the number of topics on the x-axis and the coherence score on the y-axis, I had expected to see an "elbow" (for example, here and here). In this case, however, the plot does not have a ...
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How to interpret LDA (Latent Dirichlet Allocation)?

Say I want to run topic modeling with LDA on The 20 newsgroups text dataset. So basically a dataset with texts where every text belongs to one of 20 categories. I want the LDA to split the documents ...
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53 views

Why does perplexity change with different ranges of k?

I ran a 5-fold cross-validation in R to calculate LDA perplexity for k = 2:9 using a 10% sample of my data. The output was: ...
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134 views

Using LDA to create two layered wordcloud

I create a word-cloud using LDA model what I want to do is to find the documents IDs related to that topic group. So, for example, the image here I want to allow users to click the word broccoli and ...
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11 views

Term x term matrix for text clustering. What to do with subterms of n-grams

I am doing topic discovery on a large corpus. Reading here and there I found some papers saying that in case of big, sparse document x term matrices is better to create first a term x term similarity ...
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73 views

Summary statitics to describe topic x term distribution in NLP

I created a topic model which outputted 11 topics out of 437 terms on ~60000 small documents. I wanted to show how good each topic is. But I don't know what "good" means in this case. Here's the ...
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Ideas for fixing manual misclassification

Imagine the following problem. Mechanics fix cars and use a set of parts which is recorded on invoices. They also write down a classification of the work that was done from a fixed set of possible ...
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102 views

how to prepare text data for LSTM autoencoder

My main goal is to come up with some topics using LSTM autoencoder. I want to use 20 news_group data set. after reading lots of material and looking at some GitHub project, I am still not clear how ...
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61 views

Guided LDA to categorize software requirements

I'm developing a application to categorize requirements in a requirement specification in to categories like database, front end, back end, etc. So for that I'm trying to use Guided LDA since labelled ...
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60 views

LDA visualization:

I am trying to find ways to visualize topics(LDA) to test something I am working on. I used to use pyLDAvis in python but currently what I am working on outputs only two items: 1) P(Topic | word), ...
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Combining the topics of two Latent Dirichlet Allocations

How to combine two LDAs? Let's suppose we have one corpus and we have estimated two Latent Dirichlet Allocations for two sub-corpuses : one for sub corpus A and another for sub corpus B. Now first ...
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1answer
27 views

How to understand pLSA model Q function?

I know in EM algorithm M-step, it tries to solve $$ \operatorname{argmax}_{\theta} Q(\theta, \theta^{ \text{old}}) = \sum_z p(z|x; \theta^{ \text{old}}) \log p(x,z; \theta) $$ I also understand the ...
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714 views

LDA: find percentage / number of documents per topic

I'm using LDA to find topics in a corpus. Everything works fine (I have the topics). But I would like to have the percentage / number of documents in each topic. It's possible? I looked at scikit-...
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313 views

How do I compute LDA topic similarity? [closed]

I am using the tidytext, quanteda, and tm packages in R to analyse my corpus of 220 documents. Using the topicmodels package I have extracted key topics using LDA. I now have a tidy dataframe that ...
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What are ways to build topic topic models on a corpus with high variance in text length

As the title states, I'm currently working with a corpus that has a high variance in the document length. Each input could be anything from the size of a tweet to a a full page worth of text. I'm ...
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121 views

Perplexity calculation in variational neural topic models

I'm looking at this 2016 paper from Miao et al. https://arxiv.org/abs/1511.06038 where they use a variational autoencoder for topic modelling. To evaluate the effectiveness of their model, they use ...
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1k views

Seeded LDA using topicmodels in R

I would like to perform a LDA in R by means of topicmodels. To make the resulting topics better match the topics I want to see, I would like to input "seed words" for these topics into the LDA, ...
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51 views

How to see the words shared across topics in LDA model?

I am trying to find the words that are shared across the topics and words unique to a topic using LDA model in R. Is there any package or function? Thanks
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238 views

How to implement topic modelling in regression analysis

I have a dataset consisting of hotel reviews, ratings, and other features such as traveller type, and word count of the review. I want to perform topic modeling (LDA) and use the topics derived from ...
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8k views

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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339 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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364 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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177 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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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....