You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
{{ message }}
This repository has been archived by the owner on Jan 18, 2024. It is now read-only.
As a data scientist, I want to the number of topics so that the LDA algorithm can match them with keywords in the text data I provide.
Acceptance criteria
[ ]identify total number of topics (K) in the training data
all topics are related
each word is assigned to a topic
probability across all K topics add to 1
To do:
choose the total number of topics(K)
go through each document and randomly assign each word in the comment to one the K topics
For each comment c
For each word w in c
A. For each topic t compute two things:
1) p(topic t | comment c)
2) p(word w | topic t)
B. Reassign w a new topic, choosing t with probability p(topic t | comment c) * p(word w | topic t) {(Probability that topic t generated word w)}
The text was updated successfully, but these errors were encountered:
10x Qualitative Data User story
As a data scientist, I want to the number of topics so that the LDA algorithm can match them with keywords in the text data I provide.
Acceptance criteria
To do:
A. For each topic t compute two things:
1) p(topic t | comment c)
2) p(word w | topic t)
B. Reassign w a new topic, choosing t with probability p(topic t | comment c) * p(word w | topic t) {(Probability that topic t generated word w)}
The text was updated successfully, but these errors were encountered: