predict.BTM  R Documentation 
Classify new text alongside the biterm topic model.
To infer the topics in a document, it is assumed that the topic proportions of a document is driven by the expectation of the topic proportions of biterms generated from the document.
## S3 method for class 'BTM' predict(object, newdata, type = c("sum_b", "sub_w", "mix"), ...)
object 
an object of class BTM as returned by 
newdata 
a tokenised data frame containing one row per token with 2 columns

type 
character string with the type of prediction. Either one of 'sum_b', 'sub_w' or 'mix'. Default is set to 'sum_b' as indicated in the paper, indicating to sum over the the expectation of the topic proportions of biterms generated from the document. For the other approaches, please inspect the paper. 
... 
not used 
a matrix containing containing P(zd)  the probability of the topic given the biterms.
The matrix has one row for each unique doc_id (context identifier)
which contains words part of the dictionary of the BTM model and has K columns,
one for each topic.
Xiaohui Yan, Jiafeng Guo, Yanyan Lan, Xueqi Cheng. A Biterm Topic Model For Short Text. WWW2013, https://github.com/xiaohuiyan/BTM, https://github.com/xiaohuiyan/xiaohuiyan.github.io/blob/master/paper/BTMWWW13.pdf
BTM
, terms.BTM
, logLik.BTM
library(udpipe) data("brussels_reviews_anno", package = "udpipe") x < subset(brussels_reviews_anno, language == "nl") x < subset(x, xpos %in% c("NN", "NNP", "NNS")) x < x[, c("doc_id", "lemma")] model < BTM(x, k = 5, iter = 5, trace = TRUE) scores < predict(model, newdata = x, type = "sum_b") scores < predict(model, newdata = x, type = "sub_w") scores < predict(model, newdata = x, type = "mix") head(scores)
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