Big Data Analytics - Text Analytics

In this chapter, we will be using the data scraped in the part 1 of the book. The data has text that describes profiles of freelancers, and the hourly rate they are charging in USD. The idea of the following section is to fit a model that given the skills of a freelancer, we are able to predict its hourly salary.

The following code shows how to convert the raw text that in this case has skills of a user in a bag of words matrix. For this we use an R library called tm. This means that for each word in the corpus we create variable with the amount of occurrences of each variable.

library(tm)
library(data.table)

source('text_analytics/text_analytics_functions.R')
rate = as.numeric(data$rate) keep = !is.na(rate) rate = rate[keep] ### Make bag of words of title and body X_all = bag_words(data$user_skills[keep])
X_all = removeSparseTerms(X_all, 0.999)
X_all

# <<DocumentTermMatrix (documents: 389, terms: 1422)>>
#   Non-/sparse entries: 4057/549101
# Sparsity           : 99%
# Maximal term length: 80
# Weighting          : term frequency - inverse document frequency (normalized) (tf-idf)

### Make a sparse matrix with all the data
X_all <- as_sparseMatrix(X_all)


Now that we have the text represented as a sparse matrix we can fit a model that will give a sparse solution. A good alternative for this case is using the LASSO (least absolute shrinkage and selection operator). This is a regression model that is able to select the most relevant features to predict the target.

train_inx = 1:200
X_train = X_all[train_inx, ]
y_train = rate[train_inx]
X_test = X_all[-train_inx, ]
y_test = rate[-train_inx]

# Train a regression model
library(glmnet)
fit <- cv.glmnet(x = X_train, y = y_train,
family = 'gaussian', alpha = 1,
nfolds = 3, type.measure = 'mae')
plot(fit)

# Make predictions
predictions = predict(fit, newx = X_test)
predictions = as.vector(predictions[,1])