topic-modeling

Okay, so usually topic models (such as LDA, pLSI, etc.) are used to infer topics that may be present in a set of documents, in an unsupervised fashion. I would like to know if anyone has any ideas as to how I can shoehorn my problem into an LDA framework, as there are very good tools available to solve LDA problems. For the sake of being thorough, I have the following pieces of information as input: A set of documents (segments of DNA from one organism, where each segment is a document) A document can only have one topic in this scenario A set of topics (segments of DNA from other organisms)

The current Mahout 0.8-SNAPSHOT includes a Collapsed Variational Bayes (cvb) version for Topic Modeling and removed the Latent Dirichlet Analysis (lda) approach, because cvb can be parallelized way better. Unfortunately there is only documentation for lda on how to run an example and generate meaningful output. Thus, I want to: preprocess some texts correctly run the cvb0_local version of cvb inspect the results by looking at the top n words in each of the generated topics So here are the subsequent Mahout commands I had to call in a linux shell to do it. $MAHOUT_HOME points to my mahout/bin

from nltk.tokenize import RegexpTokenizer from stop_words import get_stop_words from gensim import corpora, models import gensim import os from os import path from time import sleep import matplotlib.pyplot as plt import random from wordcloud import WordCloud, STOPWORDS tokenizer = RegexpTokenizer(r'\w+') en_stop = set(get_stop_words('en')) with open(os.path.join('c:\users\kaila\jobdescription.txt')) as f: Reader = f.read() Reader = Reader.replace("will", " ") Reader = Reader.replace("please", " ") texts = unicode(Reader, errors='replace') tdm = [] raw = texts.lower() tokens = tokenizer

I came across these 2 papers which combined collaborative filtering (Matrix factorization) and Topic modelling (LDA) to recommend users similar articles/posts based on topic terms of post/articles that users are interested in. The papers (in PDF) are: " Collaborative Topic Modeling for Recommending Scientific Articles " and " Collaborative Topic Modeling for Recommending GitHub Repositories " The new algorithm is called collaborative topic regression . I was hoping to find some python code that implemented this but to no avail. This might be a long shot but can someone show a simple python

I am getting the following error for this code: model = lda.LDA(n_topics=15, n_iter=50, random_state=1) model.fit(X) topic_word = model.topic_word_ print("type(topic_word): {}".format(type(topic_word))) print("shape: {}".format(topic_word.shape)) print ("\n") n = 15 doc_topic=model.doc_topic_ for i in range(15): print("{} (top topic: {})".format(titles[i], doc_topic[0][i].argmax())) topic_csharp=np.zeros(shape=[1,n]) np.copyto(topic_csharp,doc_topic[0][i]) for i, topic_dist in enumerate(topic_word): topic_words = np.array(vocab)[np.argsort(topic_dist)][:-(n+1):-1] print('*Topic {}\n- {}'