Questions tagged [lda]

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

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505 views

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

I am using gensim library for topic modeling, more specifically LDA. I have created my corpus, my dictionary and my lda model, and with the help of pyLDAvis library I visualize the results. When I ...
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2answers
480 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 ...
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0answers
372 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 ...
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0answers
1k views

why the accuracy of LDA model is always changing and also is high

Let’s explain the whole goal firstly, then go through the question. I am using topic modeling like LAtent Dirichlet Allocation and NMF to extract the topic from a collection of documents. My dataset ...
2
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0answers
25 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 ...
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2answers
35 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: ...
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18 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 ...
2
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0answers
48 views

Could I use the harmonic mean method to determine k number of topics when applying Latent Dirichlet Allocation using text2vec?

I am using text2vec to apply LDA on 230k docs reduced to 800 terms aprox. Is it okay to use the harmonic mean to approximate the marginal likelihood in order to mention the best topic number when that ...
2
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0answers
34 views

I have data of some movies and their subtitles.I want to classify them based on their ratings

I will convert the subtitles into vectors and use them as features to classify the movies into different categories based on their ratings.The problem that I am facing is my feature vector is much ...
2
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0answers
1k views

fitting classifier object of type 'int' has no len()

We have LDA topic modeling whose purpose is to generate a number of topics given a set of documents. So each document can belong to various topics. Also, we can evaluate the model we have created. one ...
2
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0answers
7k views

Found array with dim 3. Estimator expected <= 2

I am using LDA over a simple collection of documents. My goal is to extract topics, then use the extracted topics as features to evaluate my model. I decided to use multinomial SVM as the evaluator. ...
2
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0answers
125 views

Extending Author-Topic LDA

I've been trying to extend the LDA and wanted some help, direction and insight. Can Author-Topic LDA be used as a document "category" model? The premise of the Author-Topic model is that multiple ...
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0answers
8 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 ...
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27 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 ...
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23 views

Text Analysis : Recommendation to identify cause of loss from claim narrative documents

I am trying to analyze auto claims narrative documents which contain description about the accident usually free text written by claims executives. Is there a nlp technique I could use to identify ...
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1answer
49 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 ...
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0answers
20 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 ...
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0answers
61 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 ...
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46 views

How effective would this pseudo-LDA2Vec implementation be?

For my site I'm working on a chat recommender that would recommend chats to users. Each chat has a title and description and my corpus is composed of many of these title and description documents. I ...
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42 views

why does adding an LDA document vector with a word2vec word vector work well in LDA2vec?

In LDA the document weight vector represents the "weights" of each topic in the document. I think it's also valid to say, each row in the document vector corresponds to a word in the document, the ...
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0answers
17 views

Searching for ressource recommendations (books,papers) to validate the results of a Topic Model (LDA)?

Hi I am building a Topic Model Process with Python. To do this I am using the LDA. However I am having trouble to determine the ideal amount of topics. Currently I am using the CoherenceModel ...
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1answer
132 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
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107 views

Latent Dirichlet Allocation in R, topicmodels using VEM algorithm or Gibbs Sampling mixing tm and topicmodels library or WarpLDA from text2vec?

If I am trying to classify 230k text abstracts, which option would be better and more precise when aplying LDA?
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1answer
2k 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 ...
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0answers
110 views

Model Joint Probability of N Words Appearing Together in a Sentence

Assume that we have a large corpus of texts to train with. Given N words as input, I want to model the joint probability $p(x_1, x_2, ..., x_N)$ of these words appearing together in a sentence. More ...
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0answers
558 views

Flexible Discriminant Analysis

I am studying the book "Elements of Statistical Learning". In chapter 12 it is given about the Generalized Linear Discriminant Analysis. In one of its section it is about Flexible Discriminant ...
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722 views

Perplexity increasing on Test DataSet in LDA (Topic Modelling)

I was plotting the perplexity values on LDA models (R) by varying topic numbers. Already train and test corpus was created. Unfortunately, perplexity is increasing with increased number of topics on ...
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1answer
2k 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 ...
1
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0answers
209 views

Scikit Learn Latent Dirichlet Allocation overload my SWAP and/or RAM

1) After using LDA of scikit learn I realized it overloads the swap, but never reach more than 20% of the RAM. Do you have any idea why ? I can't really see if this is a problem, but still I would ...
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1answer
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, ...
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2answers
444 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: ...
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0answers
7 views

python kfda - methods fit and score

I'm struggling with my project, based on kfda package. The Code is here. ...
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0answers
12 views

How does LDA model do inference on new documents?

Through Latent Dirichlet Allocation (LDA) one can learn topics from texts. The trained topic model can then be used on unseen data to infer to what extent each of the learned topics are represented in ...
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17 views

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

Here's my corpus ...
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0answers
25 views

Use KL divergence to label topics 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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1answer
24 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 ...
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0answers
18 views

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
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0answers
54 views

Linking LDA topics to the input documents

I am new to LDA topic modelling. I am using gensim and am able to generate topics that make sense. Using 25k of documents, I can also print them using print_topics. ...
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2answers
416 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 ...
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1answer
376 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.
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1answer
1k views

Words from LDA output pyspark machine learning

I built a pipeline for an LDA model using pyspark's machine learning. Here is my code: ...