topic-modeling

问题 My question is related to Latent Dirichlet Allocation . Suppose we apply LDA on our dataset, then apply fit transform on that. the output is a matrix that is a collection of five documents. Each document consists of three topics. othe output is below: [[ 0.0922935 0.09218227 0.81552423] [ 0.81396651 0.09409428 0.09193921] [ 0.05265482 0.05240119 0.89494398] [ 0.05278187 0.89455775 0.05266038] [ 0.85209554 0.07338382 0.07452064]] So, this is the matrix that will be sent to a classification

问题 I'm trying to use Mallet with literally no expirience in topic modeling and etc. My purpose is to get N topics of M documents that i have right now, classify every document with one or more topic (doc 1 = topic 1; doc 2 = topic 2 and possibly topic 3) and classify with this results new document in future. I tried to use bigartm for this first, but found nothing for classification in this program, only topic modeling. So Mallet, i created a corpus.txt file with following format: Doc.num. \t

问题 Given a standard LDA model with few 1000 topics and few millions of documents, trained with Mallet / collapsed Gibbs sampler: When inferring a new document: Why not just skip sampling and simply use the term-topic counts of the model to determine the topic assignments of the new document? I understand that applying the Gibbs sampling on the new document is taking into account the topic mixture of the new document which in turn influence how topics are composed (beta, term-freq. distributions)

问题 Given a standard LDA model with few 1000 topics and few millions of documents, trained with Mallet / collapsed Gibbs sampler: When inferring a new document: Why not just skip sampling and simply use the term-topic counts of the model to determine the topic assignments of the new document? I understand that applying the Gibbs sampling on the new document is taking into account the topic mixture of the new document which in turn influence how topics are composed (beta, term-freq. distributions)

问题 Given a standard LDA model with few 1000 topics and few millions of documents, trained with Mallet / collapsed Gibbs sampler: When inferring a new document: Why not just skip sampling and simply use the term-topic counts of the model to determine the topic assignments of the new document? I understand that applying the Gibbs sampling on the new document is taking into account the topic mixture of the new document which in turn influence how topics are composed (beta, term-freq. distributions)

问题 I am trying to get a PyLDAvis graph that looks like the 2 shown in this link, that you can see right away (Intertopic Distance Map and Top 30 Most Salient Terms): http://nbviewer.jupyter.org/github/bmabey/hacker_news_topic_modelling/blob/master/HN%20Topic%20Model%20Talk.ipynb My code does display it but only partially, I can only see 1 cluster on the left and like 5-6 terms on the right, the rest gets cut off (there should be many clusters and 30 words shown). This is the code I have: import

问题 In MALLET topic modelling, the --output-topic-keys [FILENAME] option outputs beside each topic a parameter that in the tutorial in the MALLET site called "Dirichlet parameter " of the topic. I want to know what does this parameter represent? is it β in the LDA model? and if not what is it and what is it's meaning and use. I noted that when I don't use the parameter optimization option while generating the topic model, this parameter differs in version 2.0.7 than in version 2.0.8. I want to

问题 In MALLET topic modelling, the --output-topic-keys [FILENAME] option outputs beside each topic a parameter that in the tutorial in the MALLET site called "Dirichlet parameter " of the topic. I want to know what does this parameter represent? is it β in the LDA model? and if not what is it and what is it's meaning and use. I noted that when I don't use the parameter optimization option while generating the topic model, this parameter differs in version 2.0.7 than in version 2.0.8. I want to

问题 I am trying to run Mallet`s topic modelling but got the following error: Couldn't open cc.mallet.util.MalletLogger resources/logging.properties file. Perhaps the 'resources' directories weren't copied into the 'class' directory. Continuing. Exception in thread "main" java.lang.IllegalArgumentException: Trouble reading file stoplists\en.txt at cc.mallet.pipe.TokenSequenceRemoveStopwords.fileToStringArray(TokenSequenceRemoveStopwords.java:144) at cc.mallet.pipe.TokenSequenceRemoveStopwords.

问题 I'm trying to segregate the topics using lda's topic modeling. Here, I'm able to fetch the top 10 keywords for each topic. Instead of getting only top 10 keywords, I'm trying to fetch all the keywords from each topic. Can anyone please suggest me regarding the same... My Code: from gensim.models import ldamodel import gensim.corpora; from sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer; from sklearn.decomposition import LatentDirichletAllocation import warnings