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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Visualizing Author Topic Similarities: t-SNE and Cluster Labeling
I am working on a dataframe containing abstracts from various NLP conferences, along with information on information on the respective authors (names) and the keywords they've associated with their ...
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How to use Bertweet model for topic modeling
The problem is implementation of Bertweet in a topic-modeling project with understandable output like BERTopic, i want to use it on a relatively large (20k tweets) unlabelled dataset to segment it ...
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a way to automatically split a video into chapters?
Given a video with audio, we can use ASR to get a script of the sentences and timestamps. We are looking for a way to group the sentences into chapters. There are several companies that are doing it ...
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Build a topic model without data?
I need to come up with a topic model, without any labelled dataset, the model should also be multilingual, thinking of using LLM's as they are accurate and awesome but if Im to build one on my own how ...
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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 ...
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How to recreate a WE1S project?
The WE1S (WhatEvery1Says) project is so resourceful and well documented that I really want to use it. Unfortunately, I still don't know where is the repo that the Workspace documentation referring to? ...
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Is colorring each document and word as monochromatic as possible the goal of LDA in specific or all topic models in general?
In the video Training Latent Dirichlet Allocation: Gibbs Sampling, the Goal section at 7:32, the video says that:
Goal: Color each word with blue, green, red
Each article is as monochromatic as ...
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What to do when there is a jargon that is the same with a common word?
Let's say in a particular field, the word the has a specific meaning and not just be a determination. The common the one and the ...
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Identifying topic coverage from text
I have been trying to understand a feasible approach to identify if given text talks about one of the given topics(also provided as text descriptions).
for e.g.
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BERTopic: Is it okay to ignore the first two topics?
I used BERTopic to generate a topic model over a large dataset of texts. The result is very appealing and the modeled topics are mostly perfectly interpretable for a human, especially compared to ...
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Algorithm of lda2vec in NLP
I was going through lda2vec and was confused on some of the concepts.It is a combination of LDA and word2vec.Word2vec is used to learn dense word vectors and LDA is used to learn the probability ...
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How to combine topic modelling and aspect based sentiment analysis?
I have a large set of reviews on which I performed topic modelling.
Now that I have extracted topics, I would like to use them to perform Aspect Based Sentiment Analysis.
I was trying to use this ...
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How to perform topic reduction?
I am using top2vec to perform topic modelling.
According to the paper, topic reduction can be performed on the topic vectors to hierarchically group similar topics and reduce the number of topics ...
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Select Topic Words from Clusters
I am following this solution for clustering: https://towardsdatascience.com/clustering-contextual-embeddings-for-topic-model-1fb15c45b1bd
For step four "Select Topic Words from Clusters", I ...
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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 ...
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BERTopic Visualization
I new to topic modeling and I'm trying to use BERTopic inside of PyCharm. I'm struggling to ...
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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 ...
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Gensim: create a dictionary from a large corpus without loading it in RAM?
The topic modelling library Gensim offers the ability to stream a large document instead of storing it in memory.
Streaming is possible for the stage of converting the corpus to BOW, but the ...
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How to use Fuzzy Topic Model as a Classification Model Input
I have fuzzy clustering for Topic modelling and got this
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There are all total 50 topics[0 to 49] and each topic consists 30 words with a probability multiplicative factor. Now how do I make it as a ...
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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 ...
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How can I build and train mode for Arabic word embedding from scratch using BERT and share the model on hugging face?
my project is (building an Arabic word embedding model). I want to build my own model on hugging face like (aubmindlab/AraBERT model) for Arabic language using Bert for word embedding.
How can I start ...
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Topic modelling or Keyword extraction for a small dataset
I am working on a project where I have a dataset which contains very less data. These are the comments of people. I have only 130 lines with 10 words per line. My goal is to identify the common topics ...
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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 ...
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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 ...
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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 ...
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How can I implement text classification for this problem?
Given a collection of documents - each corresponding to some economic entity - I am looking to extract information and populate a table with predetermined headings. I have a small sample of this ...
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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 ...
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Grouping tweets and newspaper articles by topic
i want to implement a software that groups twitter posts to other twitter posts or to newspaper articles with similar topics.
Let's say for example someone tweets about a soccer game and at the same ...
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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 ...
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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 ...
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How does amazon's reviews that mention extracts topics from reviews?
Amazon product page contains a section called Reviews that mention. The section lists the main things that users liked or dislike about the product. For example see
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Creating a Sentiment dictionary from scratch
I am analyzing Arabic textual data from a social media forum discussing economic issues such as labor unions. I am using a package that classifies as negative, positive, or neutral.
For instance, the ...
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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 ...
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Updating a genism LDA model with new documents and topics
I have a conceptual problem that is related to a project I'm working on. I'm relatively new to the domain of NLP so this might be a poor question but I would really appreciate any help.
My dataset is ...
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Topic modelling on long documents: intra document clustering first
I have a collection (around 1000) of very noisy, similar documents, that are each very long (>10 pages - 600 paragraphs) with multiple subsections - I want to perform topic modelling across the ...
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Diachronic topic modeling with chaning set of topics
In short, the question is: how can I build a regularly updated chain of topics which would also show how topics emerge and disappear over time?
To be more precise:
I have a data with timestamps ...
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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 ...
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Measuring coherence score for Top2Vec models
I am working on creating a number of Top2Vec models on Reddit threads. I am basically changing the HDBScan cluster sizes to get different clusters of the Doc2Vec embeddings representing a different # ...
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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 ...
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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.
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Measuring Topic-coherence score & optimal number of topics in LDA Topic Modeling in Orange data mining
I'm trying to build an Orange workflow to perform LDA topic modeling for analyzing a text corpus (.CSV dataset).
Unfortunately, the LDA widget in Orange lacks for advanced settings when comparing it ...
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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 ...
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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 ...
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Best Python NLP library for supervised topic classification
I have a labeled dataset that I have ingested into a dataframe. It consists of news articles,
...
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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 ...
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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 ...
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Addressing polysemy in NLP tasks
Looking for modern algorithms using NN Language Model implementations addressing polysemy in NLP tasks, including text classification, question answering and topic modeling. Transfer/Zero-short ...
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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:
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LDA topic model has 0-weight topics, is that normal?
While experimenting with different number of topics for the Gensim implementation of LDA, I found that for a high number of topics, the output often consists of topics with all weights equal to zero. ...
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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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