LDA for each target in a binary classification problem
$begingroup$
I have a question around using LDA which may be very foolish but I am still gonna ask anyway.
Problem Statement: To classify documents into complaints and queries by processing text.
Current Approach: After preprocessing text, I am using LDA on the entire text with 20 topics and feed the probabilities to a dataframe and use a classification algorithm to predict the classes.
Which I think is the correct way to use LDA
Second Approach: (This is where the confusion lies): Does it make sense to train complaints and queries separately with 10 topics using LDA and then construct a dataframe with 20 columns(10 from complaints, 10 from queries) to pass to a classification algorithm.
In the future, this model will have process an incoming text thru both the LDA models and pass the output to a classification algorithm in order to separate complaints and queries.
I don't believe that a second approach is a valid approach but someone has suggested me so and I am trying to understand how this could be valid. Or is there a way to take 10 topics from each complaint and query from the LDA model and feed them into a dataframe in order to predict if it is a complaint or a query.
classification nlp topic-model lda binary
asked 5 mins ago
ShoaibkhanzShoaibkhanz
1012
$endgroup$
add a comment |
$begingroup$
I have a question around using LDA which may be very foolish but I am still gonna ask anyway.
Problem Statement: To classify documents into complaints and queries by processing text.
Current Approach: After preprocessing text, I am using LDA on the entire text with 20 topics and feed the probabilities to a dataframe and use a classification algorithm to predict the classes.
Which I think is the correct way to use LDA
Second Approach: (This is where the confusion lies): Does it make sense to train complaints and queries separately with 10 topics using LDA and then construct a dataframe with 20 columns(10 from complaints, 10 from queries) to pass to a classification algorithm.
In the future, this model will have process an incoming text thru both the LDA models and pass the output to a classification algorithm in order to separate complaints and queries.
I don't believe that a second approach is a valid approach but someone has suggested me so and I am trying to understand how this could be valid. Or is there a way to take 10 topics from each complaint and query from the LDA model and feed them into a dataframe in order to predict if it is a complaint or a query.
classification nlp topic-model lda binary
asked 5 mins ago
ShoaibkhanzShoaibkhanz
1012
$endgroup$
add a comment |
$begingroup$
I have a question around using LDA which may be very foolish but I am still gonna ask anyway.
Problem Statement: To classify documents into complaints and queries by processing text.
Current Approach: After preprocessing text, I am using LDA on the entire text with 20 topics and feed the probabilities to a dataframe and use a classification algorithm to predict the classes.
Which I think is the correct way to use LDA
Second Approach: (This is where the confusion lies): Does it make sense to train complaints and queries separately with 10 topics using LDA and then construct a dataframe with 20 columns(10 from complaints, 10 from queries) to pass to a classification algorithm.
In the future, this model will have process an incoming text thru both the LDA models and pass the output to a classification algorithm in order to separate complaints and queries.
I don't believe that a second approach is a valid approach but someone has suggested me so and I am trying to understand how this could be valid. Or is there a way to take 10 topics from each complaint and query from the LDA model and feed them into a dataframe in order to predict if it is a complaint or a query.
classification nlp topic-model lda binary
asked 5 mins ago
ShoaibkhanzShoaibkhanz
1012
$endgroup$
I have a question around using LDA which may be very foolish but I am still gonna ask anyway.
Problem Statement: To classify documents into complaints and queries by processing text.
Current Approach: After preprocessing text, I am using LDA on the entire text with 20 topics and feed the probabilities to a dataframe and use a classification algorithm to predict the classes.
Which I think is the correct way to use LDA
Second Approach: (This is where the confusion lies): Does it make sense to train complaints and queries separately with 10 topics using LDA and then construct a dataframe with 20 columns(10 from complaints, 10 from queries) to pass to a classification algorithm.
In the future, this model will have process an incoming text thru both the LDA models and pass the output to a classification algorithm in order to separate complaints and queries.
I don't believe that a second approach is a valid approach but someone has suggested me so and I am trying to understand how this could be valid. Or is there a way to take 10 topics from each complaint and query from the LDA model and feed them into a dataframe in order to predict if it is a complaint or a query.
classification nlp topic-model lda binary
classification nlp topic-model lda binary
asked 5 mins ago
ShoaibkhanzShoaibkhanz
1012
asked 5 mins ago
ShoaibkhanzShoaibkhanz
1012
asked 5 mins ago
ShoaibkhanzShoaibkhanz
1012
asked 5 mins ago
ShoaibkhanzShoaibkhanz
1012
asked 5 mins ago
ShoaibkhanzShoaibkhanz
1012
1012
add a comment |
add a comment |
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