data-science-topic-modeling's Introduction

data-science topic modeling - Using data science for explaining what is data science..

note: for viewing and playing with the results, click here

Data collection

This project is based on "Data Science Stack Exchange" - website which dedicated to questions and answers about data science.

And "Cross Validated" which is more focused on statistics.

To extract the tags from all the posts there I ran the following query in stack exchange's Data Explorer:

SELECT Tags 
FROM Posts
WHERE Tags IS NOT NULL

The query result looks like this:

<machine-learning><neural-network><deep-learning>
<statistics><time-series>
<machine-learning>
<python><keras><convnet><audio-recognition>
<statistics><unbalanced-classes>

Where each row represents a post.

extract transform load

Convert the data into list of lists:

(we use 2 data sources: "Data Science Stack Exchange" and "Cross Validated")

lst = []
reader = csv.reader(open('QueryResults.csv'))
for line in reader:
    lst.append(unicode(line)[3:-3].split('><'))
reader2 = csv.reader(open('QueryResults2.csv'))
for line in reader2:
    lst.append(unicode(line)[3:-3].split('><'))

After we converted the data into list of lists, we used gensim to format the data :

dictionary = gensim.corpora.Dictionary(lst)
corpus = [dictionary.doc2bow(gen_doc) for gen_doc in lst]
lda = gensim.models.ldamodel.LdaModel(corpus=corpus, id2word=dictionary, num_topics=8)

The most important parameter here is the num_topics which determine for how many topics we want to divide the model - too many topics will result in very narrow topics but too few may lead to ambiguous topics..