Simple example of classifying text in R with machine learning (text-mining library, caret, and bayesian generalized linear model). Classify. tfidf tdm term document matrix

classifytext.R

library(caret)
library(tm)

# Training data.
data <- c('Cats like to chase mice.', 'Dogs like to eat big bones.')
corpus <- VCorpus(VectorSource(data))

# Create a document term matrix.
tdm <- DocumentTermMatrix(corpus, list(removePunctuation = TRUE, stopwords = TRUE, stemming = TRUE, removeNumbers = TRUE))

# Convert to a data.frame for training and assign a classification (factor) to each document.
train <- as.matrix(tdm)
train <- cbind(train, c(0, 1))
colnames(train)[ncol(train)] <- 'y'
train <- as.data.frame(train)
train$y <- as.factor(train$y)

# Train.
fit <- train(y ~ ., data = train, method = 'bayesglm')

# Check accuracy on training.
predict(fit, newdata = train)

# Test data.
data2 <- c('Bats eat bugs.')
corpus <- VCorpus(VectorSource(data2))
tdm <- DocumentTermMatrix(corpus, control = list(dictionary = Terms(tdm), removePunctuation = TRUE, stopwords = TRUE, stemming = TRUE, removeNumbers = TRUE))
test <- as.matrix(tdm)

# Check accuracy on test.
predict(fit, newdata = test)

results.txt

> data
[1] "Cats like to chase mice."    "Dogs like to eat big bones."
> train
  big bone cat chase dog eat like mice y
1   0    0   1     1   0   0    1    1 0
2   1    1   0     0   1   1    1    0 1
> predict(fit, newdata = train)
[1] 0 1
> data2
[1] "Bats eat bugs."
> test
  big bone cat chase dog eat like mice
1   0    0   0     0   0   1    0    0
> predict(fit, newdata = test)
[1] 1
>

when you predict on test data, you get 1. what does this mean? can you briefly explain it?

There's a problem in

Train.

fit <- train(y ~ ., data = train, method = 'bayesglm')

With this output:

Error in model.frame.default(form = y ~ ., data = train, na.action = na.fail) :
invalid type (list) for variable 'y'

@gsaray101 The 1 indicates the y value. In this case, it represents "eating". A 0 would represent "not eating".

The example above has a training set of 2 records, with the y-value indicating whether the sentence is about eating or not. So, when we run the model on the test sentence, we get a 1. :)

@josvaler Be sure to copy the code as shown above. Specifically, note the type for the y column is a factor. I run the above code successfully. I'm running R 3.3.3.

> train
  big bone cat chase dog eat like mice y
1   0    0   1     1   0   0    1    1 0
2   1    1   0     0   1   1    1    0 1

Nice example - just enough to cover the concepts. I did find I had to install additional packages not listed, i.e.

library('SnowballC')
library('minqa')
library('e1071')
library('caret')
library('tm')

Thanks

The 1 indicates the y value. In this case, it represents "eating". A 0 would represent "not eating".

The example above has a training set of 2 records, with the y-value indicating whether the sentence is about eating or not. So, when we run the model on the test sentence, we get a 1.

-----My Question is where do you specify in code 'eat' is a word to predict 0 or 1. If I like to add other word "like", where should I do the changes. Please explain

how did u say that y(Dependent variable) is for eating and not Eating classes??
Why can't I consider "y" has sleeping or not-Sleeping classes?
Is it depends on terms used in the Document

what if our test data have different keywords in the text , can we classify the

suppose test data = ### "dogs are mostly of brown colour"

it is showing error

dims of 'test' and 'train' differ

hello thank you very much sharing this, but I belive
predict(fit, newdata = train) should be tested on the test set rather then train? as this link suggests : https://cran.r-project.org/web/packages/caret/vignettes/caret.html

There's a problem in

Train.

fit <- train(y ~ ., data = train, method = 'bayesglm')

With this output:

Error in model.frame.default(form = y ~ ., data = train, na.action = na.fail) :
invalid type (list) for variable 'y'

Yes, there is a problem there, because there is a function called 'train.' But, in line 12, you override that function with a data matrix. You essentially destroy the function, or replace it with a data matrix.