Questions tagged [topic-model]

A topic model describes text from a large corpus as a probability distribution over topics which are probability distributions over words. There are quantified contributions from all topics to a specific text.

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how to do topic modeling on very huge data?

When i come to know that gensim is useful library for topic modeling, I tried it on my huge amount of document. It works well only if the dictionary size is to be fix. In my case i have each and every ...
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8 views

Further analysis after topic identificatio

Using lda to find topics ...
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21 views

Which are the appropriate prameters for lda modeling?

I try to implement in R test for appropriate metrics for lda. Here the way I try to use LDA ...
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44 views

Hotel Booking Analytics: Perform an analysis in order to understand the movement of the price as the day approaches the check-in date

I am working on a hotel booking dataset. I have transactional level booking data, where each row corresponds to a booking. Please refer to below snippet of the data: I am trying to find out the ...
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40 views

Representation options of strings (keywords/topics) in models

What are all the possible ways to represent keywords in a machine learning model? The two I am aware of are: one hot encoding, using a static index. vector representation, using an embedding layer. ...
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17 views

Determining topic of text

I was wondering what I should be looking into if I want to measure the similarity between a paragraph and a corpus of text. For example, given a paragraph of text and the entire corpus of Data ...
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20 views

Topic Modelling

New to python - topic modelling, trying to include bigrams in preprocessing Had done the following, ...
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17 views

AWS comprehend service topic modelling takes too much time

I have an AWS Comprehand service in which i created an analysis job for topic modelling.Input to job was just 4.4kB text file, I got correct output after 25-30 minutes, then i tried with 750kB file it ...
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43 views

Find specific topics with topic modelling

I am looking for a way to classifiy text automatically by specific topics, i don´t have labeled data. Is this a possible/usual method of achieving this? If not, what would be better? Topic Modelling ...
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18 views

Which approach to select category based on keywords

I want to assign a certain category to a group of keywords. So i.e. people can upload images or videos, when they do this they can set keywords for this. These keywords are free to type so words can ...
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17 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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27 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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Should my LDA topic model be skewed towards only one topic? If not then how can I un-skew it?

I'm building LDA topic models in to apply against a collection of small texts and regardless of the number of topics, I'm finding that there is always one topic that is very large (in terms of ...
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How to find out the subject of an email (in the form of a sentence) or a pdf document in NLP using Python

How to find out the subject of an email (in the form of a sentence) or a pdf document in NLP using Python. If I do topic modelling and get different groups of topic, how do I pick out the only topic ...
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171 views

Online vs Batch Learning in Latent Dirichlet Allocation using Scikit Learn

Reference: https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.LatentDirichletAllocation.html I'm looking at the LDA algorithm from Scikit Learn for topic modeling. Can someone ...
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30 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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24 views

Perform Topic Modeling By Creating a Function and Pass Along to Purrr : map() using R

My goal is to perform topic modeling (splitting into 3 topics each) by grouping words by word.trend column rather than copy and pasting block of codes for each word.trend as I have 800 unique values ...
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1answer
474 views

NLP algorithms for categorizing a list of words with specific topics

Currently I am using LDA to apply topic modeling to a corpus. Since LDA is unsupervised, it returns a set of words for a given 'topic' but doesn't necessarily specify the topic itself. I was wondering ...
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137 views

Is it correct to create topic models using both train and test data?

I have a dataset of text documents splitted into train and test sets. My task is a binary classification, classifying these documents to either 1 or -1. I have already computed some features using TF-...
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40 views

Topic Segmentation - should it be done in Raw, TfIdf or Semantic Space?

Let's assume we have a collection of documents and wish to perform some unsupervised topic segmentation. As always, we will perform some preprocessing (including tokenization, accent-removal, ...
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683 views

How to give names/labels to topics in LDA [duplicate]

I want to give labels to different topics created using LDA. I don't want to do it manually. I saw some papers on automatic labeling but I am still confused. How can I use the information produced ...
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231 views

What's beyond topic modeling?

I tried topic modeling (LDA, NMF) to extract insights from the data. I'm curious right now, are there other methods for unsupervised learning to cluster documents by the same or similar context? (...
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1answer
11k views

Resume Parsing - extracting skills from resume using Machine Learning

I am trying to extract a skill set of an employee from his/her resume. I have resumes stored as plain text in Database. I do not have predefined skills in this case. How should I approach this problem?...
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59 views

Evaluating the result of topic modeling in a way that time matters

I have run different topic modeling approach on my data(its clinical data related to Cognitive impairment diseases. we are going to process what thing is important that make it develop to more harsh ...
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19 views

How to build News Tagging model(s)

I am trying to build a news tagging system. Given a piece of news article, find 5-6 key terms from the news article that best describe the article. Refer to the image below from google news. What are ...
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477 views

How to compare the topic coherence between models of different number of topics?

If I'm not mistaken, in this paper here http://svn.aksw.org/papers/2015/WSDM_Topic_Evaluation/public.pdf it appears that topics with larger number of topics will inherently have larger coherence ...
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127 views

How to split natural language script into segments?

I have a bunch of .txt and .srt files extracted from a MOOC website, they are the scripts of the videos. I would like to segment the scripts into parts such that each part falls into one of the ...
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294 views

Why do we need the hyperparameters beta and alpha in LDA?

I'm trying to understand the technical part of Latent Dirichlet Allocation (LDA), but I have a few questions on my mind: First: Why do we need to add alpha and gamma every time we sample the equation ...
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2answers
273 views

Guided topic modeling: generating words from topics

I need to generate lists of words related to specific topics for a project. I am familiar with clustering methods of topic modeling such as LDA, but I have something else in mind. Are there any ...
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81 views

Performance Metric for topic extraction when there is no ground truth

I am extracting topics from text using a predefined ontology containing 2690 concepts, wordnet(to expand concept terms with their synsets, and other morphological forms of the same word) and lucene to ...
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2answers
98 views

Collection Of Variable Length Sequences and Descriptions: A Search Problem

I have a tough problem and need some advice: Suppose I have a collection of variable length sequences, many of which are unique -- imagine the moves to a chess game, eg d4 Nf6 c4 g6 Nc3 Bg7 ...
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2k views

What is the difference between topic modeling and clustering?

I know that topic modeling and clustering are related, but not similar techniques. Can anyone suggest what are the main differences?
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354 views

Automatic question categorization when we know important words in each category

I am currently working on a question categorization problem where I automatically want to assign a category to the question. The question set I have is unlabelled. The categories for the problem are ...
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529 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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435 views

What are key dataset requirements for topic models and word embeddings?

I have a dataset of 2000 documents where avg doc size is 300 words. The vocab is dominated by domain-specific words. My goal is to find similar documents. For this, I tried LDA, LSI, Doc2Vec (topics=...
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1answer
1k 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 ...
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2answers
880 views

Compare two topic modelling sets

I have two sets of newspaper articles where I train the first newspaper dataset separately to get the topics per each newspaper article. ...
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218 views

Hellinger Distance in Gensim

I have set of documents as follows where each document has set of words that represents the content of it. ...
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1answer
431 views

Combine two sets of clusters

I have two sets of topics obtained from two different sets of news paper articles. In other words, Cluster_1 = ${x_1, x_2, ..., x_n}$ includes the main topics of 'X' news paper set and Cluster_2 = ${...
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1answer
225 views

Equally sized topics in Latent Dirichlet allocation

I'm using the topicmodels package for R to cluster a big set of short texts (between 10-75 words) into topics. After manually reviewing a few models it seems like there are 20 realtivly stable topics. ...
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934 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 ...
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2answers
3k views

Why we should not feed LDA with tfidf

Can someone explain why we can not feed LDA topic model with TFIDF? What is wrong with this approach conceptually?
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1answer
2k views

Apply SVM on LDA in python

hope someone kindly put time here, my approach is like this: TFIDF -> LDA -> SVM I am using LDA to extract topics. I want to do topic modelling and use the topics as features to do document ...
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1answer
554 views

News topic detection and categorization

If I want to get how many and what kind of topics are covered by New York Times each week from a bag of words model(All the news covered by NYT in a week) how should I approach? Using traditional ...
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116 views

What is “energy spectrum” in Latent Semantic Indexing (LSI)?

What is meant by energy spectrum in LSI(Latent Semantic Indexing)? I am doing topic modeling with gensim's LsiModel, and part of the output per chunk is the following: ...
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3k views

How to use Non Negative Matric Factorization (NMF)'s transform method to project topics on new text data

I have created a NMF topic model in python the code snippet for which is as follows: ...
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1answer
155 views

Quantifying the Reproducibility of LDA Models

I am working on a text mining project where I'm using Latent Dirichlet Allocation to study a corpus of documents. I'm currently in the process of optimizing my parameters to get the best models for my ...
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1answer
288 views

Determine document novelty/similarity with the aid of Latent Dirichlet allocation (LDA) or Named Entities

Given an index or database with a lot of (short) documents (~ 1 million), I am trying to do some kind of novelty detection for each newly incoming document. I know that I have to compute the ...
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1answer
278 views

i have to classify an sms into categories like educational, bank related,etc. Is this a problem of topic modelling or text classification? [closed]

If this is a problem of text classification, is a similar dataset available or i have to make one on my own?? I have a dataset of emails for classification into spam/ham.
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486 views

Comparing two Corpora using Topic Model

I want to compare two corpora (two different collections of texts) using Topic Modeling. I trained the model separately on the two collections and manually matched similar topics based on their ...