This is a short tutorial for using WordCram, a Processing library, to generate word clouds from an LDA instance. The LDA analysis will be done using Stephen Hansen's Topic Modelling library available at https://github.com/sekhansen/text-mining-tutorial.
First, Processing will need to be installed on your machine. The link for the download can be found at: https://processing.org/download/?processing
After installation, make sure you know where the processing-java file was downloaded. This will be needed for the Python module.
First, let's generate the ldaobj instance from the topicmodels.LDA class.
"""
(c) 2014, Stephen Hansen, stephen.hansen@upf.edu
Python script for tutorial illustrating collapsed Gibbs sampling for Latent Dirichlet Allocation.
See explanation for commands on http://nbviewer.ipython.org/url/www.econ.upf.edu/~shansen/tutorial_notebook.ipynb.
"""
import pandas as pd
import topicmodels
import sys
from wordclouds import *
topic_nums = 10
########## select data on which to run topic model #########
data = pd.read_table("speech_data_extend.txt",encoding="utf-8")
########## clean documents #########
docsobj = topicmodels.RawDocs(data.speech, "stopwords.txt")
docsobj.token_clean(1)
docsobj.stopword_remove("tokens")
docsobj.stem()
docsobj.tf_idf("stems")
docsobj.stopword_remove("stems",100)
all_stems = [s for d in docsobj.stems for s in d]
print("number of unique stems = %d" % len(set(all_stems)))
print("number of total stems = %d" % len(all_stems))
########## estimate topic model #########
ldaobj = topicmodels.LDA(docsobj.stems,topic_nums)
ldaobj.sample(0,20,10)
ldaobj.sample(0,20,10)
ldaobj.samples_keep(4)
ldaobj.topic_content(20)
# Choose how many of the last chains to keep - here it is 4
ldaobj.samples_keep(4)
number of unique stems = 100
number of total stems = 12436
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###Make WordClouds
The function make_word_clouds contains 2 required arguments, the ldaobj instance and the path to Processing.
The Processing path typically looks like "processing-2.2.1/processing-java".
The other 2 arguments are to tweak the aesthetics of the PDF files generated for each word cloud.
weight_interval tells Python what size to make both the largest and smallest weighted words, and adjust the in-between weights accordingly. Typically, if there is a lot of dispersion in weights or frequencies, then weight_interval=(4,140) (DEFAULT) will result in nice looking charts. If the output is only showing a fraction of the words, then trying weight_interval=(4,50) will likely fix the issue.
max_words is the maximum number of words you would like to appear in each word cloud. By default it is set to 250. Reducing this number may require adjusting the weight_interval variable accordingly so that all remaining words fit well in the image.
make_word_clouds(ldaobj,"~/Downloads/processing-2.2.1/processing-java",weight_interval=(4,140))
That is it for now. For any questions or suggestions, please email me at paul.soto@upf.edu