lda

I am using Mallet api to extract topic from twitter data and I have already extracted topics which are seems good topic. But I am facing problem to estimating K. For example I fixed K value from 10 to 100. So, I have taken different number of topics from the data. But, now I would like to estimate which K is best. There are some algorithm I know as Perplexity Empirical likelihood Marginal likelihood (Harmonic mean method) Silhouette I found a method model.estimate() which may be used to estimate with different value of K. But I am not getting any idea to show the value of K is best for the

All: I am beginner in R topic modeling, it all started three weeks ago. So my problem is I can successfully processed my data into corpus, Document term matrix and LDA function. I have tweets as my input and about 460,000 tweets. But I am not happy with the result, the words across all topic are very similar. packages <- c('tm','topicmodels','SnowballC','RWeka','rJava') if (length(setdiff(packages, rownames(installed.packages()))) > 0) { install.packages(setdiff(packages, rownames(installed.packages()))) } options( java.parameters = "-Xmx4g" ) library(tm) library(topicmodels) library(SnowballC

I was following the instructions on this link (" http://radimrehurek.com/2014/03/tutorial-on-mallet-in-python/ "), however I came across an error when I tried to train the model: model = models.LdaMallet(mallet_path, corpus, num_topics =10, id2word = corpus.dictionary) IOError: [Errno 2] No such file or directory: 'c:\\users\\brlu\\appdata\\local\\temp\\c6a13a_state.mallet.gz' Please share any thoughts you might have. Thanks. This can happen for two reasons: 1. You have space in your mallet path. 2. There is no MALLET_HOME environment variable. Make sure that mallet properly works from command

Recently, I implemented Gibbs sampling for LDA topic model on Python using numpy, taking as a reference some code from a site. In each iteration of Gibbs sampling, we remove one (current) word, sample a new topic for that word according to a posterior conditional probability distribution inferred from the LDA model, and update word-topic counts, as follows: for m, doc in enumerate(docs): #m: doc id for n, t in enumerate(doc): #n: id of word inside document, t: id of the word globally # discount counts for word t with associated topic z z = z_m_n[m][n] n_m_z[m][z] -= 1 n_z_t[z, t] -= 1 n_z[z] -

I want to save the LDA model from pyspark ml-clustering package and apply the model to the training & test data-set after saving. However results diverge despite setting a seed. My code is the following: 1) Import packages from pyspark.ml.clustering import LocalLDAModel, DistributedLDAModel from pyspark.ml.feature import CountVectorizer , IDF 2) Preparing the dataset countVectors = CountVectorizer(inputCol="requester_instruction_words_filtered_complete", outputCol="raw_features", vocabSize=5000, minDF=10.0) cv_model = countVectors.fit(tokenized_stopwords_sample_df) result_tf = cv_model

可以将文章内容翻译成中文,广告屏蔽插件可能会导致该功能失效(如失效，请关闭广告屏蔽插件后再试): 由 翻译 强力驱动 问题: I have studied LDA and Topic model for several weeks.But due to my poor mathematics ability, i can not fully understand its inner algorithms.I have used the GibbsLDA implementation, input a lot of documents, and set topic number as 100, i got a file named "final.theta" which stores the topic proportion of each topic in each document.This result is good, i can use the topic proportion to do many other things. But when i tried Blei's C language implementation on LDA, i only got a file named final.gamma, but i don't know how to