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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58
votes
6answers
26k 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 ...
27
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3answers
19k views

What is difference between text classification and topic models?

I know the difference between clustering and classification in machine learning, but I don't understand the difference between text classification and topic modeling for documents. Can I use topic ...
24
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1answer
7k views

NLP - why is “not” a stop word?

I am trying to remove stop words before performing topic modeling. I noticed that some negation words (not, nor, never, none etc..) are usually considered to be stop words. For example, NLTK, spacy ...
22
votes
2answers
28k 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 ...
10
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1answer
5k 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?
9
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5answers
11k views

Tutorials on topic models and LDA

I would like to know if you people have some good tutorials (fast and straightforward) about topic models and LDA, teaching intuitively how to set some parameters, what they mean and if possible, with ...
9
votes
2answers
14k 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?
8
votes
1answer
21k 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?...
7
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4answers
5k views

BERT: it is possible to use it for topic modeling?

I'm struggling to understand which are the full capabilities of BERT: it is possible to make topic modeling of text, like the one we can achieve with LDA?
6
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4answers
5k views

How to give name to topics created using LDA?

I have categorized 800,000 documents into 500 categories using the Mahout topic modelling. Instead of representing the topic using the top 5/10 words for each topics, I want to infer a generic name ...
6
votes
1answer
310 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 ...
6
votes
1answer
718 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 ...
5
votes
1answer
180 views

Would Topic Modelling be classified as NLP or NLU?

I recently started my journey into the world of NLP, it's been one heck of a ride. I'm currently trying to understand whether topic modelling would be considered as NLP or NLU. Initially I would ...
5
votes
3answers
2k views

Need help with LDA for selecting features

I am currently selecting features of products by using LDA to group 6000 keywords of product into topics. Here is the sample of my dataset after being organized into list of keywords for each product ...
5
votes
1answer
885 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 = ${...
5
votes
2answers
359 views

Why do my Latent Dirichlet Allocation Topics mix words that never co-occurred?

I have one corpus of documents on diabetes, another on Leonardo da Vinci, and another on animation and computer graphics. I combined all of these documents into a LDA and got a topic like the one ...
5
votes
0answers
510 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 ...
5
votes
1answer
1k 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 ...
4
votes
2answers
6k views

NLTK Sklearn Genism Text to Topic

I aint no data scientist/machine learner. What Im Lookin for ...
4
votes
1answer
58 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 ...
4
votes
3answers
150 views

How can I discover topics in a social media data-set?

I'm working on a project and i need to discover topics existing in a social media data set. For instance, i wanna extract the topics existing on 200K tweets. Any one recommend to me any machine ...
4
votes
2answers
1k 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. ...
4
votes
1answer
452 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 ...
4
votes
1answer
2k views

How can I run Labeled LDA over one textual document?

I have 200K tweets and I already a applied the LDA (Latent Dirichlet Allocation) algorithm using Gensim python library. And now I need to apply over them the labeled/supervised LDA. Can any one help ...
4
votes
2answers
377 views

Topic models for Relevance Prediction

Suppose I have data in the form of Query/Document Pairs, along with corresponding relevance scores (or class labels). Is there a way to use topic modeling to devise a model so that later given a ...
3
votes
1answer
68 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 ...
3
votes
2answers
107 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 ...
3
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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 ...
3
votes
3answers
2k views

scikit-learn - Should I fit model with TF or TF-IDF?

I am trying to find out the best way to fit different probabilistic models (like Latent Dirichlet Allocation, Non-negative Matrix Factorization, etc) on sklearn (Python). Looking at the example in ...
2
votes
2answers
366 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? (...
2
votes
3answers
2k views

replicability / reproducibility in topic modeling (LDA)

If I'm not wrong, topic modeling (LDA) is not replicable, i.e. it gives different results in different runs. Where does this come from (where does this randomness come from and why is it necessary?) ...
2
votes
1answer
21 views

Do weights of keywords for each topic add up to 1 in topic modeling?

When you run a topic modeling (say LDA), you can get outputs for some number of topics with corresponding keywords and their weights. Based on my understanding, people usually output top 10 or top 20 ...
2
votes
2answers
463 views

How to discard trash topics from topic models?

I am undertaking text analysis of some twitter data. In the end I want to have a data that is interpretable. And so in the end I would like to reduce the data to relevant unit of analysis. Topic ...
2
votes
1answer
197 views

Reasons and prevention of trivial (and less trivial) misclassification errors?

I was not sure about posting this question with mentioning the name of the company, which I quite respect and admire. However, I've figured that a wider exposure might help the team to fix this and ...
2
votes
1answer
28 views

Tweet Classification into topics- What to do with data

Good evening, First of all, I want to apologize if the title is misleading. I have a dataset made of around 60000 tweets, their date and time as well as the username. I need to classify them into ...
2
votes
1answer
960 views

Online vs Batch Learning in Latent Dirichlet Allocation using Scikit Learn

Reference I'm looking at the LDA algorithm from Scikit Learn for topic modeling. Can someone tell me how the 'online' method of learning works vs the 'batch' method of learning? Also, what is learning ...
2
votes
1answer
3k 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 ...
2
votes
1answer
406 views

What metrics must i use in my data(unstructured) preprocessing research?

i am currently working on preprocessing unstructured data (emails,logs,bug reports and irc chats). i wish to prove preprocessing improves the content quality. are there metrics available to prove ...
2
votes
1answer
269 views

Automatic topic labelling for topic modelling

I am just curious to know if there is a way to automatically get the lables for the topics in Topic modelling. It would be really helpful if there's any python implementation of it.
2
votes
0answers
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
votes
1answer
2k 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 ...
2
votes
1answer
264 views

How to get columns from unsorted rows in Pandas? (MALLET)

My data (the doc-topics output from a MALLET topic model) has the following shape: ...
2
votes
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 ...
1
vote
1answer
631 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 ...
1
vote
1answer
247 views

TF-IDF for Topic Modeling

Can TF-IDF be used a sole method for Topic Modeling ? (I know there are better methods like LDA , LSA etc) I just want to understand if TF-IDF alone can help us in Topic modeling . If yes , can ...
1
vote
2answers
41 views

Reaching 100% accurray in Data Mining

I am currently working with Topic Models, especially LDA, and now I am asking myself if it's possible to reach total accurracy regarding the results. If I insepct the results of my Topic Model, the ...
1
vote
1answer
370 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. ...
1
vote
2answers
96 views

Clustering Small Text Descriptions

Im presented with a unique text classification problem. Im given a list of descriptions each containing 3-8 words. I know that there are some descriptions that are nearly the same, but the majority ...
1
vote
1answer
44 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. ...
1
vote
1answer
20 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: ...