8
$\begingroup$

I am using the gensim library for topic modeling, more specifically LDA. I created my corpus, my dictionary, and my LDA model. With the help of the pyLDAvis library I visualized the results. When I print the words with the highest probability on appearing to a topic with pprint(lda_model.print_topics()) I have results for the first topic similar to:

$0.066*\text{car} + 0.032*\text{gas} + 0.031*\text{model} + 0.031*\text{top} + 0.024*\text{CO2} \ + \ ... \ + \ 0.012*\text{investment}$

The results are good as are indicative about the topic, but when I interact with the relevance parameter ($\lambda$ - lambda value) provided by pyLDAvis, I can have results that are more specific about the topic, for example setting $\lambda=0.2$ the top 5 words are:

car, horsepower, torque, speed, V8

My question: is there any function or parameter in gensim that can return the pair probability - word given a specific lambda value?

$\endgroup$

1 Answer 1

0
$\begingroup$

According this SO post this is the way:

lambd = 0.6 # a specific relevance metric value

all_topics = {}
num_topics = lda_model.num_topics 
num_terms = 10 

for i in range(1,num_topics+1): ## Correct range
    topic = LDAvis_prepared.topic_info[LDAvis_prepared.topic_info.Category == 'Topic'+str(i)].copy()
    topic['relevance'] = topic['loglift']*(1-lambd)+topic['logprob']*lambd
    all_topics['Topic '+str(i)] = topic.sort_values(by='relevance', ascending=False).Term[:num_terms].values
pd.DataFrame(all_topics)

```
$\endgroup$

Your Answer

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

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