topic_number: The Matrix of Words and Weight for Topics
In BeaJJ/ComTxt: Twitter text analysis (ComTxt)
Description
Usage
Arguments
Source
Examples
Description
This function retrive the best topic modelling K by HarmonicMean, Coherenc, and perplexity score
the algorithm will calculate a HarmonicMean, Coherenc, and perplexity score score to allow us to choose the best topics from 1 to k. The scoure presents the probabilistic coherence of each topic. This gives us the quality of the topics being produced.
Usage
1
Arguments
df
The preprocessed twitter data
Source
David Mimno (2013) mallet: A wrapper around the Java machine learning tool MALLET
Examples
1
2
df_ pre <- twitter_preprocess(df_tweets, ud_lang = "spanish", stopwords_lang = "es")
topic_number(df)
BeaJJ/ComTxt documentation built on Dec. 17, 2021, 10:46 a.m.
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Description Usage Arguments Source Examples
Description
This function retrive the best topic modelling K by HarmonicMean, Coherenc, and perplexity score the algorithm will calculate a HarmonicMean, Coherenc, and perplexity score score to allow us to choose the best topics from 1 to k. The scoure presents the probabilistic coherence of each topic. This gives us the quality of the topics being produced.
Usage
1 |
Arguments
df |
The preprocessed twitter data |
Source
David Mimno (2013) mallet: A wrapper around the Java machine learning tool MALLET
Examples
1 2 | df_ pre <- twitter_preprocess(df_tweets, ud_lang = "spanish", stopwords_lang = "es")
topic_number(df)
|
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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