Topic Term Matrix about labeled-lda-python

[JoeZJH]

You can output them in order with the following code:
the topic list: print llda_model.topics
the term(word) list: print llda_model.terms

[nabilariefm-hi]

aaa i see2 thankyouu again!!

[nabilariefm-hi]

Halo Joe, I really confused with the result, why the beta (topic-word) result same number (probability) for every label (my label is rating 1,2,3,4,5). Do you know why? Should I increase the iteration number? Really appreciate your answer because this is for my undergraduate thesis. Thankyouu fyi : differensial is (5+4) - (1+2) [image: messageImage_1660958430922.jpg] -- Nabila Arief Muharomah Undergraduate Student of Engineering Management Bandung Institute of Technology Indonesia

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On Sun, Aug 14, 2022 at 6:27 PM Jiahong ***@***.***> wrote: You can output them in order with the following code: the topic list: print llda_model.topics the term(word) list: print llda_model.terms — Reply to this email directly, view it on GitHub <#9 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/A2JQHIMLSWU6DBUSJUP634DVZDJZTANCNFSM56PU7YEA> . You are receiving this because you authored the thread.Message ID: ***@***.***>

[nabilariefm-hi]

Halo Joe, I really confused with the result, why the beta (topic-word)
result same number (probability) for every label (my label is rating
1,2,3,4,5). Do you know why? Should I increase the iteration number? Really
appreciate your answer because this is for my undergraduate thesis.
Thankyouu
fyi : differensial is (5+4) - (1+2)

[JoeZJH]

How many iterations did you use? may be you can try more iterations.

[nabilariefm-hi]

I use 1000 iteration

[JoeZJH]

Probably because you have too little data.

[nabilariefm-hi]

aaah i see2 maybe thats the reason. Also I want to ask which one better I use to train a model? the (1) or (2)?

Training

(1) --> #llda_model.training(iteration=100, log=True)
(2) --> #while True:
print("iteration %s sampling..." % (llda_model.iteration + 1))
llda_model.training(1)
print("after iteration: %s, perplexity: %s" % (llda_model.iteration, llda_model.perplexity()))
print("delta beta: %s" % llda_model.delta_beta)
if llda_model.is_convergent(method="beta", delta=0.01):
break

[JoeZJH]

Both of them will be ok. If you do not know how many iterations it takes for the model to converge, you can use (2) to get a convergent model.

[nabilariefm-hi]

Aaaah I see2 , okee Joe thankyouu so much for all the answer😊

[JoeZJH]

Happy to help!