How do I print lda topic model and the word cloud of each of the topics

Question

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 t         


        

Answer 1

Assuming you have trained a gensim lda model you can simply create a word cloud with the following code

# lda is assumed to be the variable holding the LdaModel object
import matplotlib.pyplot as plt
for t in range(lda.num_topics):
    plt.figure()
    plt.imshow(WordCloud().fit_words(lda.show_topic(t, 200)))
    plt.axis("off")
    plt.title("Topic #" + str(t))
    plt.show()

I will highlight a few mistakes on your code so you can better follow what I have written above.

WordCloud().generate(something) expects something to be raw text. It will tokenize it, lowercase it and remove stop words and then compute the word cloud. You need the word sizes to match their probability in a topic (I assume).

lda.print_topics(8, 200) returns a textual representation of the topics as in prob1*"token1" + prob2*"token2" + ... you need the lda.show_topic(topic, num_words) to get the word with the corresponding probability as tuples. Then you need WordCloud().fit_words() to generate the word cloud.

The following code is your code with the above visualization. I would also like to point out that you are inferring topics from a single document which is very uncommon and probably not what you wanted.

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.tokenize(raw)
stopped_tokens = [i for i in tokens if not i in en_stop]
tdm.append(stopped_tokens)

dictionary = corpora.Dictionary(tdm)
corpus = [dictionary.doc2bow(i) for i in tdm]
ldamodel = gensim.models.ldamodel.LdaModel(corpus, num_topics=8, id2word = dictionary)
for t in range(ldamodel.num_topics):
    plt.figure()
    plt.imshow(WordCloud().fit_words(ldamodel.show_topic(t, 200)))
    plt.axis("off")
    plt.title("Topic #" + str(t))
    plt.show()

Although from a different library you can see topic visualizations with corresponding code for what the result will be (Disclaimer: I am on of the authors of that library).