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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Making linear models in R [closed]
I'm trying to make linear models in R, using the practicing datasets (sim1, sim2, sim3, sim4). So I used this code,
mod1 <- lm(y ~ x1 + x2, data = sim3)
mod1
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Using Word Embeddings in Clustering and Topic Modelling
I am new to the field of NLP and would appreciate any guidance please. I am trying to understand how word embeddings can be used in clustering and topic modelling. If I create word embeddings for ...
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LDA Document Topic Distribution Prediction for Unseen Document
Popular python libraries for topic modeling like gensim or sklearn allow us to predict the topic-distribution for an unseen document, but I have a few questions on what's going on under the hood. I've ...
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Understanding Variational inference for LDA
I am trying to derive from scratch variational inference for LDA. I am following this course: https://home.cs.colorado.edu/~jbg/teaching/CSCI_5622/19a.pdf
When computing $p(Z|\Theta)$ they do the ...
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Input for LSI, LDA and HDP?
It seems that the Latent Semantic Indexing (LSI or Latent Semantic Analysis, LSA) can take as input information frequency data (such as TfIdf-weighted space), e.g., according to this documentation ...
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Why does latent dirichlet allocation (LDA) fail when dealing with large and heavy-tailed vocabularies?
I'm reading the 2019 paper Topic Modeling in Embedding Spaces which claims that the embedded topic model improves on these limitations of LDA. But why does LDA have these limitations—why does it fail ...
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How to use confusion matrix to evaluate accuracy of a topic model (LDA)?
Here's my understanding of topic model, suppose there are 4 topics generated from running LDA from the cleaned corpus(user reviews).
<Topic j: word1(prob.), word2(prob.), word3(prob.)>
Generated ...
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What is a word embedding approach that would work for these pre-labeled documents?
My Situation:
I should start off with my end goal: I want to get a distance metric between each document and all of the other documents
To get there, I first need to encode these topic labels so that ...
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fine tune universal-sentence-encoder embeddings
I am new to NLP and Neural Networks. I want to do topic analysis for a dataset of reviews of our product. I tried to use the universal-sentence-encoder along with <...
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Normalizing Topic Vectors in Top2vec
I am trying to understand how Top2Vec works. I have some questions about the code that I could not find an answer for in the paper. A summary of what the algorithm does is that it:
embeds words and ...
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is there neural network architecture for unsupervised learning for topic modelling?
Let say i have a bunch of document and I want to analyse the topic of the corpus. The only way i know is to use unsupervised learning with gensim which use model like Latent Dirichlet allocation(LDA),...
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Structural topic modeling - compare groups (issue ranking)
I am trying to compare two groups (liberal news, conservative news articles) after running a structural topic modeling. I first did a structural topic modeling of the whole news article and got a ...
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why is using a small vocabulary for topic modelling bad?
i am trying to classify texts into topics. for example, let's say one of the topics is cooperation. so in the vocab param of the sklearn api. so some of the prevalent words (or "tokens" are ...
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Statistical significance of distance between difference of vectors (LDA)
I have an output from Latent Dirichlet Allocation (LDA) which sums up the corpus of about 2,000 documents into 50 topics. The 2,000 documents belong to a category each (in total there are 4 categories,...
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What are the current approaches to topic modelling (i.e. better than LSA, LDA, LSI)?
I am looking into methods for topic modeling with the purpose of keyword generation. Given a corpus consisting of multiple documents, I would like to get a list of semantically relevant and ...
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Topic modeling with time data
I want to predict the topic of a project tracker narrative entry to identify stages of the project. This entry is written by employees describing what they did in the project.
The projects are ...
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Semi-Supervised Hierarchical Topic Model
Problem statement:
I'm looking to label some data with topics. These topics have a hierarchical structure (3 layers deep at maximum, but I have leaf nodes 2 layers down as well) that I have been given....
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Meaning of $P(d)$ in pLSA
Given the following equation:
$$
{\displaystyle P(w,d)=P(d)\sum _{c}P(c|d)P(w|c)}
$$
How $P(d)$ is calculated? I suggest that's relative length of the document with respect to corpus:
$$
P(d)=\frac{\...
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Interpretation of negative values in the results of LSA
How can I interpret the results of LSA?
From the following table, I can understand that documents 0 and 1 (dealing with cats) fall into Topic 2.
Document 4, which talks about both dog and cat, falls ...
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In Latent Dirichlet allocation, is the following formula the probability of observing a single document, or an entire corpus?
This is the formula in question:
Source: https://en.wikipedia.org/wiki/Latent_Dirichlet_allocation
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Topic modeling for regression
Is there a way to influence the way topics are created with topic modelling in the sense that the topics also reflect their influence on the target variable of a machine learning problem?
I have a ...
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Latent Dirichlet Allocation - dimensionality of the Dirichlet prior parameter
I seeking some clarity on the dimensionality of the (hyper)parameter $\eta$ of the "smoothed LDA" model in Section 5.4 of the original paper by Blei, Ng, Jordan (2003), which can be found ...
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Using KL divergence to select topic from LDA
I have used sklearn's sklearn.decomposition.LatentDirichletAllocation module to model 10 topics in a set of documents.
I have also used the same on a reference text (1 document) and obtained a 1 topic ...
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Suggestions for identifying the most "important" image labels
I have a table with images and their assigned object (i.e. specific sub-parts) and image (i.e. image as a whole) labels. Each image may have multiple object labels but only 2 whole-image labels. I ...
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How to know if LDA(Latent Dirichlet Allocation) is applicable to a particular problem? [closed]
Suppose my database has many tables and each table has many columns. I want to use column names of all tables to figure out "major data contents" in my database. Example columns are ...
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Compare text corpora
I have am currently performing speech recognition experiments on 2 different corpora. I have the ground truth human-labelled texts for both corpora. I am performing different experiments that lead to ...
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Auto scoring sales calls
I am not a statistician so pardon any incorrect technical term usage. I have learned just enough to know I don't know anything but am trying to understand the problem before engaging a real life ...
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Updating an unsupervised model but retaining similarity
In my example I am using topic modelling (specifically a version of LDA) although I think avenues for exploring this could relate to other unsupervised techniques like clustering.
I train a model and ...
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Is there a way to get the optimal cutoff points based on probability of topic models and the outcomes?
I have topic models probability obtained using LDA topic models method. I’d like to use these probabilities for 5 topics to predict an ...
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Topic modelling when the "documents" are not static and new tokens are generated regularly
This question is related to this one and this one. As background, I'm working with Magic: the Gathering decklist data; I'm trying to automatically classify decks into different archetypes based on the ...
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Are 1-tailed 2-sample t tests suitable to assess if external validity performance is statistically significant better than a random model?
Some context:
I currently have two groups of 50 text documents. Each group is about a different subject and curated by people. As such, I assume the two groups to be my ground truth.
I mix the two ...
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parameter estimation on the LDA model
I have a problem with estimating the parameters of $\theta$, and $\phi$ in the Latent Dirichlet Allocation (LDA) model. The article Finding scientific topics has done the estimation of the parameters ...
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Can Latent Dirichlet Allocation (LDA) be used to generate word embeddings?
In the original Word2Vec paper (Efficient Estimation of Word Representations in Vector Space, Mikolov et al. 2013), I came across this phrase:
Many different types of models were proposed for ...
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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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