DocumentTerm Matrix Clustering Suggestions
$begingroup$
I am trying to use NLP-style techniques to create clusters of similar books based on their "taglines" or "synopses". The problem I am having is that for many of the synopses, only one word is common. For example:
Book 1: "A drifter commits a murder and goes on the run from the authorities".
Book 2: "Analyzing the history of murder in New York City".
Just from the synopses, you can see the two books are quite different, but are matched on the word "murder", hence, they are being clustered together.
Is there any parameter or technique to be used in DocumentTermMatrix or a similar function (I am using the R package) that can provide a more accurate clustering?
Thanks.
r nlp
asked 57 mins ago
Thomas MooreThomas Moore
1011
$endgroup$
add a comment |
$begingroup$
I am trying to use NLP-style techniques to create clusters of similar books based on their "taglines" or "synopses". The problem I am having is that for many of the synopses, only one word is common. For example:
Book 1: "A drifter commits a murder and goes on the run from the authorities".
Book 2: "Analyzing the history of murder in New York City".
Just from the synopses, you can see the two books are quite different, but are matched on the word "murder", hence, they are being clustered together.
Is there any parameter or technique to be used in DocumentTermMatrix or a similar function (I am using the R package) that can provide a more accurate clustering?
Thanks.
r nlp
asked 57 mins ago
Thomas MooreThomas Moore
1011
$endgroup$
add a comment |
$begingroup$
I am trying to use NLP-style techniques to create clusters of similar books based on their "taglines" or "synopses". The problem I am having is that for many of the synopses, only one word is common. For example:
Book 1: "A drifter commits a murder and goes on the run from the authorities".
Book 2: "Analyzing the history of murder in New York City".
Just from the synopses, you can see the two books are quite different, but are matched on the word "murder", hence, they are being clustered together.
Is there any parameter or technique to be used in DocumentTermMatrix or a similar function (I am using the R package) that can provide a more accurate clustering?
Thanks.
r nlp
asked 57 mins ago
Thomas MooreThomas Moore
1011
$endgroup$
I am trying to use NLP-style techniques to create clusters of similar books based on their "taglines" or "synopses". The problem I am having is that for many of the synopses, only one word is common. For example:
Book 1: "A drifter commits a murder and goes on the run from the authorities".
Book 2: "Analyzing the history of murder in New York City".
Just from the synopses, you can see the two books are quite different, but are matched on the word "murder", hence, they are being clustered together.
Is there any parameter or technique to be used in DocumentTermMatrix or a similar function (I am using the R package) that can provide a more accurate clustering?
Thanks.
r nlp
r nlp
asked 57 mins ago
Thomas MooreThomas Moore
1011
asked 57 mins ago
Thomas MooreThomas Moore
1011
asked 57 mins ago
Thomas MooreThomas Moore
1011
asked 57 mins ago
Thomas MooreThomas Moore
1011
asked 57 mins ago
Thomas MooreThomas Moore
1011
1011
add a comment |
add a comment |
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