How to cluster documents under topics using latent semantic analysis (lsa)

Question

I've been working on latent semantic analysis (lsa) and applied this example: https://radimrehurek.com/gensim/tut2.html

It includes the terms clustering under topics but couldn't find anything how we can cluster documents under topics.

In that example, it says that 'It appears that according to LSI, “trees”, “graph” and “minors” are all related words (and contribute the most to the direction of the first topic), while the second topic practically concerns itself with all the other words. As expected, the first five documents are more strongly related to the second topic while the remaining four documents to the first topic'.

How can we relate those five documents with Python code to the related topic?

You can find my python code below. I would appreciate any help.

from numpy import asarray
from gensim import corpora, models, similarities

#https://radimrehurek.com/gensim/tut2.html
documents = ["Human machine interface for lab abc computer applications",
             "A survey of user opinion of computer system response time",
             "The EPS user interface management system",
             "System and human system engineering testing of EPS",
             "Relation of user perceived response time to error measurement",
             "The generation of random binary unordered trees",
             "The intersection graph of paths in trees",
             "Graph minors IV Widths of trees and well quasi ordering",
             "Graph minors A survey"]

# remove common words and tokenize
stoplist = set('for a of the and to in'.split())
texts = [[word for word in document.lower().split() if word not in stoplist]
         for document in documents]

# remove words that appear only once
all_tokens = sum(texts, [])
tokens_once = set(word for word in set(all_tokens) if all_tokens.count(word) == 1)

texts = [[word for word in text if word not in tokens_once] for text in texts]

dictionary = corpora.Dictionary(texts)
corp = [dictionary.doc2bow(text) for text in texts]

tfidf = models.TfidfModel(corp) # step 1 -- initialize a model
corpus_tfidf = tfidf[corp]

# extract 400 LSI topics; use the default one-pass algorithm
lsi = models.lsimodel.LsiModel(corpus=corp, id2word=dictionary, num_topics=2)

corpus_lsi = lsi[corpus_tfidf]


#for i in range(0, lsi.num_topics-1):
for i in range(0, 3):
    print lsi.print_topics(i)

for doc in corpus_lsi: # both bow->tfidf and tfidf->lsi transformations are actually executed here, on the fly
    print(doc)

Answer 1

corpus_lsi has a list of 9 vectors, which is the number of documents. Each vector stores in at its i-th index the likeliness that this document belongs to topic i. If you just want to assign a document to 1 topic, choose the topic-index with the highest value in your vector.