README.md
In BTM: Biterm Topic Models for Short Text

BTM - Biterm Topic Modelling for Short Text with R

This is an R package wrapping the C++ code available at https://github.com/xiaohuiyan/BTM for constructing a Biterm Topic Model (BTM). This model models word-word co-occurrences patterns (e.g., biterms).

Topic modelling using biterms is particularly good for finding topics in short texts (as occurs in short survey answers or twitter data).

This R package is on CRAN, just install it with install.packages('BTM')

The Biterm Topic Model (BTM) is a word co-occurrence based topic model that learns topics by modeling word-word co-occurrences patterns (e.g., biterms)

A biterm consists of two words co-occurring in the same context, for example, in the same short text window.
BTM models the biterm occurrences in a corpus (unlike LDA models which model the word occurrences in a document).
It's a generative model. In the generation procedure, a biterm is generated by drawing two words independently from a same topic z. In other words, the distribution of a biterm b=(wi,wj) is defined as: P(b) = sum_k{P(wi|z)*P(wj|z)*P(z)} where k is the number of topics you want to extract.
Estimation of the topic model is done with the Gibbs sampling algorithm. Where estimates are provided for P(w|k)=phi and P(z)=theta.

More detail can be referred to the following paper:

Xiaohui Yan, Jiafeng Guo, Yanyan Lan, Xueqi Cheng. A Biterm Topic Model For Short Text. WWW2013. https://github.com/xiaohuiyan/xiaohuiyan.github.io/blob/master/paper/BTM-WWW13.pdf

library(udpipe)
library(BTM)
data("brussels_reviews_anno", package = "udpipe")

## Taking only nouns of Dutch data
x <- subset(brussels_reviews_anno, language == "nl")
x <- subset(x, xpos %in% c("NN", "NNP", "NNS"))
x <- x[, c("doc_id", "lemma")]

## Building the model
set.seed(321)
model  <- BTM(x, k = 3, beta = 0.01, iter = 1000, trace = 100)

## Inspect the model - topic frequency + conditional term probabilities
model$theta
[1] 0.3406998 0.2413721 0.4179281

topicterms <- terms(model, top_n = 10)
topicterms
[[1]]
         token probability
1  appartement  0.06168297
2      brussel  0.04057012
3        kamer  0.02372442
4      centrum  0.01550855
5      locatie  0.01547671
6         stad  0.01229227
7        buurt  0.01181460
8     verblijf  0.01155985
9         huis  0.01111402
10         dag  0.01041345

[[2]]
         token probability
1  appartement  0.05687312
2      brussel  0.01888307
3        buurt  0.01883812
4        kamer  0.01465696
5     verblijf  0.01339812
6     badkamer  0.01285862
7   slaapkamer  0.01276870
8          dag  0.01213928
9          bed  0.01195945
10        raam  0.01164474

[[3]]
         token probability
1  appartement 0.061804812
2      brussel 0.035873377
3      centrum 0.022193831
4         huis 0.020091282
5        buurt 0.019935537
6     verblijf 0.018611710
7     aanrader 0.014614272
8        kamer 0.011447470
9      locatie 0.010902365
10      keuken 0.009448751
scores <- predict(model, newdata = x)

Make a specific topic called the background

# If you set background to TRUE
# The first topic is set to a background topic that equals to the empirical word distribution. 
# This can be used to filter out common words.
set.seed(321)
model      <- BTM(x, k = 5, beta = 0.01, background = TRUE, iter = 1000, trace = 100)
topicterms <- terms(model, top_n = 5)
topicterms

Can be done using the textplot package (https://github.com/bnosac/textplot), which can be found at CRAN as well (https://cran.r-project.org/package=textplot)
An example visualisation built on a model of all R packages from the Natural Language Processing and Machine Learning task views is shown above (see also https://www.bnosac.be/index.php/blog/98-biterm-topic-modelling-for-short-texts)

library(textplot)
library(ggraph)
library(concaveman)
plot(model)

An interesting use case of this package is to

cluster based on parts of speech tags like nouns and adjectives which can be found in the text in the neighbourhood of one another
cluster dependency relationships provided by NLP tools like udpipe (https://CRAN.R-project.org/package=udpipe)

This can be done by providing your own set of biterms to cluster upon.

Example clustering cooccurrences of nouns/adjectives

library(data.table)
library(udpipe)
## Annotate text with parts of speech tags
data("brussels_reviews", package = "udpipe")
anno <- subset(brussels_reviews, language %in% "nl")
anno <- data.frame(doc_id = anno$id, text = anno$feedback, stringsAsFactors = FALSE)
anno <- udpipe(anno, "dutch", trace = 10)

## Get cooccurrences of nouns / adjectives and proper nouns
biterms <- as.data.table(anno)
biterms <- biterms[, cooccurrence(x = lemma, 
                                  relevant = upos %in% c("NOUN", "PROPN", "ADJ"),
                                  skipgram = 2), 
                   by = list(doc_id)]

## Build the model
set.seed(123456)
x     <- subset(anno, upos %in% c("NOUN", "PROPN", "ADJ"))
x     <- x[, c("doc_id", "lemma")]
model <- BTM(x, k = 5, beta = 0.01, iter = 2000, background = TRUE, 
             biterms = biterms, trace = 100)
topicterms <- terms(model, top_n = 5)
topicterms

Example clustering dependency relationships

library(udpipe)
library(tm)
library(data.table)
data("brussels_reviews", package = "udpipe")
exclude <- stopwords("nl")

## Do annotation on Dutch text
anno <- subset(brussels_reviews, language %in% "nl")
anno <- data.frame(doc_id = anno$id, text = anno$feedback, stringsAsFactors = FALSE)
anno <- udpipe(anno, "dutch", trace = 10)
anno <- setDT(anno)
anno <- merge(anno, anno, 
              by.x = c("doc_id", "paragraph_id", "sentence_id", "head_token_id"), 
              by.y = c("doc_id", "paragraph_id", "sentence_id", "token_id"), 
              all.x = TRUE, all.y = FALSE, suffixes = c("", "_parent"), sort = FALSE)

## Specify a set of relationships you are interested in (e.g. objects of a verb)
anno$relevant <- anno$dep_rel %in% c("obj") & !is.na(anno$lemma_parent)
biterms <- subset(anno, relevant == TRUE)
biterms <- data.frame(doc_id = biterms$doc_id, 
                      term1 = biterms$lemma, 
                      term2 = biterms$lemma_parent,
                      cooc = 1, 
                      stringsAsFactors = FALSE)
biterms <- subset(biterms, !term1 %in% exclude & !term2 %in% exclude)

## Put in x only terms whch were used in the biterms object such that frequency stats of terms can be computed in BTM
anno <- anno[, keep := relevant | (token_id %in% head_token_id[relevant == TRUE]), by = list(doc_id, paragraph_id, sentence_id)]
x    <- subset(anno, keep == TRUE, select = c("doc_id", "lemma"))
x    <- subset(x, !lemma %in% exclude)

## Build the topic model
model <- BTM(data = x, 
             biterms = biterms, 
             k = 6, iter = 2000, background = FALSE, trace = 100)
topicterms <- terms(model, top_n = 5)
topicterms

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