CalculateHighestTopicCosineSimilarity: Calculate Maximimum Likehood Topic
In sailuh/topicflowr: Topic Flow
Description
Usage
Arguments
Value
Description
Accepts two topic term matrices generated by different LDA runs.
Topics may have different terms in their respective vocabulary.
Non-existent terms will be added with probability 0 to each other.
See CalculateTopicFlow for an example of it's usage.
Usage
1
CalculateHighestTopicCosineSimilarity(ttm1, ttm2)
Arguments
ttm1
A topic term matrix
ttm2
A topic term matrix (e.g. of the following month)
Value
Returns a cartesian product data.table of the ttm pair containing the topics with maximum likehood.
sailuh/topicflowr documentation built on May 27, 2019, 8:46 a.m.
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Description Usage Arguments Value
Description
Accepts two topic term matrices generated by different LDA runs.
Topics may have different terms in their respective vocabulary.
Non-existent terms will be added with probability 0 to each other.
See CalculateTopicFlow for an example of it's usage.
Usage
1 | CalculateHighestTopicCosineSimilarity(ttm1, ttm2)
|
Arguments
ttm1 |
A topic term matrix |
ttm2 |
A topic term matrix (e.g. of the following month) |
Value
Returns a cartesian product data.table of the ttm pair containing the topics with maximum likehood.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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