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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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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Systems outputting embedding vectors of multiple diverse top-N concepts?

In some classification tasks (e.g. object identification, text named entity recognition), sometimes we'd like to: have the system consider many potential output classes (>>10000 .. 100M), ...
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Sample size for doing topic modeling using LDA() in R (topicmodels package)

I just started to learn and do text analysis for open-text survey questions. My sample size is around 2000. I want to use the LDA() function in R (topicmodels package) to identify topics among the ...
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How should data be organized for topic modelling

Say I have 10 documents with 10 sentences each. I'm curious how should my raw data look like? Is there a standard? Should it be like? ...
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Using Topic Modeling for Text Classification

I'm trying to use topic modeling using Latent Dirichlet Allocation as input for text classification problem. Although, I'm not getting good results by doing this. The data has three variables doc id, ...
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37 views

Perplexity Score for Topic modelling

I am new to topic modelling using LDA. I build a initial version of topic modelling. I tried different topic models. I got to know that perplexity score is a good measure for evaluating topic models. ...
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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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I'm trying to identify the posterior distributions in LDiA. Are theta and phi in this PGM the posterior distributions of LDiA?

My understanding is that alpha and beta are the dirichlet priors, and the posteriors would be theta and phi. And since theta and phi are from the same family of distributions as alpha and beta, the ...
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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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Difference between Structural Topic Modeling(STM) and SAGE (Sparse Additive Generative Model)?

I have read that STM combines 3 models of: (1) correlated topic model (CTM) (2) Dirichlet-Multinomial Regression (DMR) topic model (3) Sparse Additive Generative Model (SAGE) Is it correct to just ...
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Does the number of topics change based on the type of model?

I am working on a classification of some tweets regarding biomasses. I have made a classification with LDA using 75 topics (found by using the ldatuning library). Everything went alright, topics were ...
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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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Number of topics - Topic Model(BTM)

I would like to know if it could be a good idea, in order to find the optimal number of topics in a topic model (in my particular case a BTM), to look at the mean of the topics coherence scores and ...
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BTM - Classification per topic

I have been given a task in which I have to classify some tweets per topic. I have created , in R, a model using BTM, which means I have the probability of each word belonging to each topic. Shouldn't ...
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No “elbow” with LDA measures c_v & uMass

I am doing some topic modelling experiments - for learning purposes only - with LDA against just over 50k hotel reviews. I am using the c_v and uMass coherence scores but I see no significant change ...
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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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online LDA with R (topic modelling)

Topic modelling based on online LDA continously updates the model with new data without having to refit the model using the full dataset, ideally without an a priori known vocabulary. This makes it ...
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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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60 views

Gibbs Sampling in LDA - any other inference procedures?

I am playing with the LDA model in python and would like to ask if anyone knows whether Python's toolkits for LDA (gensim/scikit-learn), have all implemented the Gibbs Sampling inside or is there any ...
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Topic Modeling: topic coherence score by each topic

I'm currently using NMF, but I am just curious whether I can have coherence scores for each topic. For instance, i want to see whether topic 5 is trained better than topic 1. By looking at the ...
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Interpretation of token weights in LDA topic model?

I'm using Gensim's LDA model for some topic modeling. Once the model has trained, it provides for each topic a coherence score, as well as a list of tuples of weights and tokens. The latter could for ...
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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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Browser history topic clustering

I have a detailed browser history where each event has a visit time, visit duration, URL, and page title. For example let's consider this browser history (page titles only): is Thailand open for ...
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Extract Keyword/Concept From Column Description Using NLP

Suppose in my database, each table has a description associated with each column and I want to further extract keyword or key concept from the description. For example, mean of transaction amount in ...
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Balanced corpus in topic models

I am building a topic model for a corpus of webpages extracted from a random subset of domains, the topics seems to be ok, but often I see very similar topics, which makes me think I should reduce the ...
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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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Choosing Gensim- Phrases parameters

I'm using the gensim.models.phrases in order to find bigrams in my data. For what I know,the main hyperparameters are: min_count and threshold. I have found ...
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Use mutual information score on multi label problem

I have a dataset in which each observation is assigned multiple topics, which also have a probability / confidence metric. I would like to evaluate my topic model using not just the top 1 assigned ...
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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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Can you restrict the terms Latent Dirichlet Allocation(LDA) uses in specific topics?

I admit I do not have the full understanding of the inner-working of Latent-Dirichlet Allocation(LDA) yet. I have used the sklearn implementation on my text corpus. ...
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Predicting links between documents with topic models

I hope this is the right forum for this sort of question: I am trying to find a topic model that allows me to predict links between documents. Basically, I am trying to figure out if the topics in my ...
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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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Sentiment about topics (defined by LDA)

For a project I do a research about topics in online reviews, and the effect of these topics on the rating (by doing a regression). Furthermore and the reason I would like to have your help: I would ...
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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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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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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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293 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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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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