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Question

I have a structured dataset with columns 'text' and 'topic'. Someone has already conducted a word embedding/topic modeling so each row in 'text' is assigned a topic number (1-200). I would like to create a new data frame with the topic number and the top 5-10 key words that represent that topic.

I've done this before, but I usually start from scratch and run an LDA model. Then use the objects created by the LDA to find keywords per topic. That said, I'm starting from a mid-point that my supervisor gave me, and it's throwing me off.

The data structure looks like below:

import pandas as pd
df = pd.DataFrame({'text': ['foo bar baz', 'blah bling', 'foo'], 
               'topic': [1, 2, 1]})

So would the plan be to create a bag of words, groupby 'topic,' and count the words? Or is there a keywords function and group by a column option that I don't know about in gensim or nltk?

Answer 1

I have created a dictionary where keys are the topic and text is the string of words appending each topic's words.

d = dict()
for index, ser in df.iterrows():
    print(index, df.loc[index]['text'])
    topic  = df.loc[index]['topic']
    if topic not in d.keys():
        d[df.loc[index]['topic']] = ""
    d[df.loc[index]['topic']] += ( df.loc[index]['text']) + " "

print(d)
#Output
{1: 'foo bar baz foo ', 2: 'blah bling '}

Then I have used the Counter package to get frequency of words for each topic.

from collections import Counter
for key in d.keys():
    print(Counter(d[key].split()))

#Output
Counter({'foo': 2, 'baz': 1, 'bar': 1})
Counter({'blah': 1, 'bling': 1})

Answer 2

I think this works:

test = pd.DataFrame(df.groupby("topic")['document'].apply(lambda documents: ''.join(str(documents))))

from nltk import Metric, Rake

r = Rake(ranking_metric= Metric.DEGREE_TO_FREQUENCY_RATIO, language= 'english', min_length=1, max_length=4)

r.extract_keywords_from_text(test.document[180])
r.get_ranked_phrases()

I just need to figure out how to loop in through for each topic and append it to a dataframe.