1
$\begingroup$

I am trying to perform topic extraction in a panda dataframe. I am using LDA topic modeling in order to extract the topics in my dataframe. No problem.

But, I would like to apply LDA topic modeling to each row in my dataframe.

Current datafame:

date cust_id words
3/14/2019 100001 samantha slip skirt pi ski
1/21/2020 10002 steel skirt solid greenish
5/19/2020 10003 arizona denim blouse d

The dataframe I am looking for:

date cust_id words topic 0 words topic 0 weights
3/14/2019 100001 samantha slip skirt pi ski skirt 0.5
1/21/2020 10002 skirt solid greenish greenish 0.2
5/19/2020 10003 arizona denim blouse denim 01

vectorizer = CountVectorizer(max_df=0.9, min_df=20, token_pattern='\w+|\$[\d.]+|\S+')

tf = vectorizer.fit_transform(features['words']).toarray()

tf_feature_names = vectorizer.get_feature_names()

number_of_topics = 6 model = LatentDirichletAllocation(n_components=number_of_topics, random_state=1111)

model.fit(tf)


I tried to merge two dataframe together, it does not work.
How will I be able to add each topic in each column and add each topic weights to add to all my rows?

I posted the question in stackoverflow: https://stackoverflow.com/questions/71476309/topic-modelling-in-an-existing-dataframe-in-python

$\endgroup$

1 Answer 1

0
$\begingroup$

You can try this:

def format_topics_sentences(ldamodel=lda_model, corpus=corpus, texts=data):
   
    sent_topics_df = pd.DataFrame()

   
    for i, row in enumerate(ldamodel[corpus]):
        row = sorted(row, key=lambda x: (x[1]), reverse=True)
        # Get the Dominant topic, Perc Contribution and Keywords for each document
        for j, (topic_num, prop_topic) in enumerate(row):
            if j == 0:  # -- dominant topic
                wp = ldamodel.show_topic(topic_num)
                topic_keywords = ", ".join([word for word, prop in wp])
                sent_topics_df = sent_topics_df.append(pd.Series([int(topic_num), round(prop_topic,4), topic_keywords]), ignore_index=True)
            else:
                break
    sent_topics_df.columns = ['Dominant_Topic', 'Perc_Contribution', 'Topic_Keywords']

    # Add original text to the end of the output
    
    contents = pd.Series(texts)
    sent_topics_df = pd.concat([sent_topics_df, contents], axis=1)
    return(sent_topics_df)
    

df_topic_sents_keywords = format_topics_sentences(ldamodel=optimal_model, corpus=corpus, texts=df)


df_dominant_topic = df_topic_sents_keywords.reset_index()
df_dominant_topic.columns = ['Document_No', 'Dominant_Topic', 'Topic_Perc_Contrib', 'Keywords', 'Text']


df_dominant_topic.head(5)

You can find the detailed implementation in this Kaggle Notebook

$\endgroup$
1
  • $\begingroup$ I did go another way, but this is awesome. I'm sure, I will use it for fun. THANK YOU LOVE!!!!!! I did share the answer on Twitter. $\endgroup$ Apr 26, 2022 at 3:13

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge that you have read and understand our privacy policy and code of conduct.

Not the answer you're looking for? Browse other questions tagged or ask your own question.