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Questions tagged [lda]

Latent Dirichlet Allocation (LDA) is an algorithm in the field of topic modeling.

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2 votes
1 answer
52 views

Dealing with high dimensionality datasets

I have data of dimensionality (25000, 100, 500) i.e. 25000 rows each consisting of a 2 dimensional 100 X 500 matrix. Currently I am only applying CNN for ...
5 votes
1 answer
2k views

How to choose threshold for gensim Phrases when generating bigrams?

I'm generating bigrams with from gensim.models.phrases, which I'll use downstream with TF-IDF and/or gensim.LDA ...
1 vote
1 answer
142 views

Choice of the number of topics (clusters) in textual data

I have a social science background and I'm doing a text mining project. I'm looking for advice about the choice of the number of topics/clusters when analyzing textual data. In particular, I'm ...
0 votes
1 answer
393 views

Topic modelling with many synonyms - how to extract 'latent themes'

Here's my corpus ...
1 vote
1 answer
416 views

Implementation of LDA (Latent Dirichlet Allocation) for classification tasks

Until now I have used LDA only for topic modelling. I would like to know which is the simplest implementation of LDA algorithm for classification tasks.
2 votes
1 answer
2k views

How to construct the document-topic matrix using the word-topic and topic-word matrix calculated using Latent Dirichlet Allocation?

How to construct the document-topic matrix using the word-topic and topic-word matrix calculated using Latent Dirichlet Allocation? I can not seem to find it anywhere, even not from the author of LDA, ...
1 vote
1 answer
3k views

How to map topic to a document after topic modeling is done with LDA

Is there any way I can map generated topic from LDA to the list of documents and identify to which topic it belongs to ? I am interested in clustering documents using unsupervised learning and ...
0 votes
3 answers
731 views

Apply Labeled LDA on large data

I'm using a dataset contains about 1.5M document. Each document comes with some keywords describing the topics of this document(Thus multi-labelled). Each document belongs to some authors(not just one ...
8 votes
1 answer
1k views

Gensim LDA model: return keywords based on relevance (λ - lambda) value

I am using the gensim library for topic modeling, more specifically LDA. I created my corpus, my dictionary, and my LDA model. With the help of the pyLDAvis library I visualized the results. When I ...
2 votes
3 answers
248 views

How to identify text similarity based on training data?

I have a set of documents (1 to 11) for which the labeling is done. Lets Assume: ...
1 vote
1 answer
130 views

Topic alignment / topic modelling

What is the most efficient method for detecting whether the article is mostly about a specific topic, but without lots of data for training? My task is to determine how much a document is e.g. about ...
0 votes
0 answers
9 views

Choosing the right number of tokens in a dictionary for an LDA topic modelling analysis

I have a data set of customers with n complaints. As a result, I want to perform topic modelling to find topics that customers talk about in their complaints. For this I use LDA from spacy. I have ...
0 votes
0 answers
10 views

Finding good parameters for topic modeling using LDA and spacy

I have a data set of customers with n complaints [c_1,...,c_n]. As a result, I want to perform topic modelling to find topics that customers talk about in their complaints. For this I use LDA from ...
0 votes
0 answers
6 views

Semantic grouping and replacement of words to improve topic modelling with LDA

I have a data set of customers with complaints. As a result, I want to perform topic modelling to find topics that customers talk about in their complaints. I use LDA for this. In the results of LDA, ...
1 vote
2 answers
4k views

How to Combine tfidf with LSTM in keras?

I am classifying emails as spam or ham using LSTM and some of its modified form(by adding constitutional layer at the end). For converting documents into vectors I am using ...
4 votes
2 answers
1k views

Can I do incremental learning with the sklearn implementation of Linear Discriminant Analysis

I have a large number of pictures that I would like to use LDA on. However, it requires too much memory, so I was wondering if it would be possible to make the learning incremental, using a sklearn ...
17 votes
3 answers
25k views

Why should we not feed LDA with TF-IDF input?

Can someone explain why we can not feed LDA topic model with TFIDF? What is wrong with this approach conceptually?
0 votes
0 answers
43 views

number of topics in LDA model (coherence)

I want to fit a LDA model using RStudio, but there are some trouble in the determination of topics number. Perplexity and coherence are suggested to do this work, so first I use LDA and perplexity ...
1 vote
2 answers
540 views

Memory error - Hierarchical Dirichlet Process, HDP gensim

I am running Hierarchical Dirichlet Process, HDP using gensim in Python but as my corpus is too large it is throwing me following error: ...
2 votes
2 answers
883 views

Topic Modeling: LDA vs LSA vs ToPMine

I am new to Topic Modeling. Is it possible to implement ToPMine in Python? In a quick search, I can't seem to find any Python package with ToPMine. Is ToPMine better than LDA and LSA? I am aware ...
0 votes
0 answers
26 views

LDA calculations manually

my question: has anyone ever done LDA calculations manually? I have difficulty in manual calculation. can someone help me to teach me for lda calculations manually.
1 vote
0 answers
201 views

Short Text Topic Modelling in Python

I have a large dataset of short reviews and I would like to find the most recurring themes. For this reason, I got into topic modeling. I am looking for some good tutorials and references for short ...
0 votes
1 answer
16 views

method of allocating documents to pre-defined classifications

I'm looking for a method of allocating documents (30K and growing) to a set of some 200 categories. The categories will be user defined and will grow over time. As my data is unlabelled my thought ...
1 vote
2 answers
107 views

Topic Modeling - n-grams or 1,2,3,...n-grams?

Do people use n-grams or 1,2,3,...n-grams in both matrix factorisation and generative models in Topic Modeling? I've been trying to understand the basics of Topic Modeling and came to know that there ...
1 vote
0 answers
114 views

Trying to use term document matrix as input to orange 3

I have a CSV file that has tokens as columns and documents as rows, where the rest of the cells are ints that represent term frequency. I'm trying to use this as input into Orange 3, but Orange 3 ...
0 votes
1 answer
200 views

Lost human names after 'Lemmatization' for topic modeling in python

I'm using gensim in Python for topic modeling. Currently, I have one problem. If I don't lemmatize, human names will appear as 'Most Relevant Terms for Topic,' but after lemmatization, the human names ...
1 vote
1 answer
103 views

document similarity using LDA probabilities

Let us say I have a LDA model trained on a corpus of text. I would like to know, for a newly given document, which one from the corpus is closet to it. But, to do so, I want to use probabilities ...
0 votes
0 answers
158 views

List of words cluster by topics

I have a list of words, these words correspond to labels in news and they are not duplicated. I would like to get a clustering for this list based on topics. I try with wordnet but I don't know how ...
1 vote
1 answer
299 views

reuse of LDA model for new data

I am working with the LDA (Latent Dirichlet Allocation) model from sklearn and I have a question about reusing the model I have. After training my model with data how do I use it to make a prediction ...
25 votes
2 answers
36k views

What does the alpha and beta hyperparameters contribute to in Latent Dirichlet allocation?

LDA has two hyperparameters, tuning them changes the induced topics. What does the alpha and beta hyperparameters contribute to LDA? How does the topic change if one or the other hyperparameters ...
0 votes
0 answers
44 views

Are the word of women and men different when expressing their views on the same subject?

My data includes women's comments on X and Y and men's comments on X and Y. Each comment is of equal length. I will calculate how much different the word choice between men and women when commenting ...
2 votes
1 answer
816 views

extract document topic vectors from lda model

how can I extract document-topic matrix from LDA model and use it as input features an svm classifier? I am using gensim for implementation
1 vote
1 answer
2k views

Topic Modelling in an existing dataframe in python

I am trying to perform topic extraction in a panda dataframe. I am using LDA topic modeling in order to extract the topics in my dataframe. No problem. But, I would like to apply LDA topic modeling ...
2 votes
1 answer
46 views

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 ...
1 vote
0 answers
21 views

Two sets of topics/words in Topic Modeling

In short, the question is: I have two sets of words per document. I would like to extract two sets of topics per document corresponding to sets of words. To be more precise: Document(d) can be ...
1 vote
1 answer
101 views

Latent Dirichlet Allocation (LDA) importance of document generation and Gibbs Sampling

I am having trouble finding the correlation between the two seemingly uncorrelated parts of LDA. What I understood from several videos is: There is a document generation "part", which is ...
3 votes
1 answer
193 views

How to generate synthetic text for LDA?

I am wanting to play around with LDA topic modelling, namely looking at the effects of document length, topic number etc all have on accuracy (I know it has been done elsewhere, but no one seems to ...
1 vote
1 answer
452 views

In scikit-learn's LDA implementation, how can I sort the topics by frequency over the entire corpus?

I've used scikit-learn to perform LDA topic modeling, and I'd ultimately like to sort the topics by saliency/frequency over the entire corpus, but I'm unsure how to do as such. I've used pyldavis ...
1 vote
1 answer
166 views

K-means and LDA for text classification

I hope to explain in a clear way what I would like to do. I have more than 50000 tweets and I would like to add some labels on topics. So I have used LDA for doing this. I have also used k-means to ...
1 vote
1 answer
97 views

What does "intractable" mean for this function in latent Dirichlet allocation (LDA)?

In the original paper Latent Dirichlet Allocation, the authors said that the function $$p(\mathbf{w} \mid \alpha, \beta)=\frac{\Gamma\left(\sum_{i} \alpha_{i}\right)}{\prod_{i} \Gamma\left(\alpha_{i}\...
0 votes
0 answers
36 views

LSA Model Improvement

I followed gensim's Core Tutorial and build an LSA Classification, topic modeling and Document Similarity model for newsgroups dataset. My code is available here. I need help with below 3 concepts. ...
0 votes
1 answer
43 views

Which tool is good to collect tweets on 50 keywords over the last 5 years and then analyze them with the LDA algorithm or sentiment analysis?

I want to find tweets from the last 5 years to a topic. For this I decide for 50 Keywords (related to the main topic), where I want to find data on Twitter. I want to find out how the trend on the ...
2 votes
0 answers
81 views

What hyperparameter values does the LDA mallet model use by default? Is it true that the formula to calculate alpha = 5.0/n(topics)?

I am trying to figure out the default $\alpha$ & $\eta$ values used by mallet LDA, but there is not a lot of information on this. I did find a couple of answers, with no proper references, saying ...
1 vote
0 answers
33 views

Modeling of topics orthogonal to a given patterns

How to force the topics to be different from the defined ones? Suppose I have a collection of texts about cats and dogs.There should naturally be two topics: one about dogs and one about cats. But I'm ...
2 votes
0 answers
49 views

Evaluate Topic Modelling on synthetic data

I try to find the optimal number of topics on a synthetic corpus (so a list of lists of tokens I generate using various parameters). I, therefore, know the true number of topics and the true topics ...
5 votes
1 answer
1k views

Calculating optimal number of topics for topic modeling (LDA)

am going to do topic modeling via LDA. I run my commands to see the optimal number of topics. The output was as follows: It is a bit different from any other plots that I have ever seen. Do you think ...
1 vote
1 answer
349 views

What does updated alpha mean in LDA model?

I'm trying to understand LDA model by reading through implementations of the algorithm. Many implementations update alpha during training iterations with codes like: ...
1 vote
0 answers
92 views

Best measure to indicate quality of LDA model

On my corpora, I am running LDA with different settings (I experiment with different number of topics, different different ngrams and TFIDF or regular BOW). Now, I want to rank these setups to select ...
1 vote
0 answers
58 views

Self-supervised learning for automatic labeling of data using LDA and Word2Vec

I am trying to implement this paper A Brand-New Look at You: Predicting Brand Personality in Social Media Networks with Machine Learning for labeling Twitter data of brands with a corresponding brand ...
62 votes
6 answers
33k views

Latent Dirichlet Allocation vs Hierarchical Dirichlet Process

Latent Dirichlet Allocation (LDA) and Hierarchical Dirichlet Process (HDP) are both topic modeling processes. The major difference is LDA requires the specification of the number of topics, and HDP ...