How to create unigrams, bigrams and n-grams of App Reviews

This is one of the frequent questions I’ve heard from the first timer NLP / Text Analytics - programmers (or as the world likes it to be called “Data Scientists”). Prerequisite For simplicity, this post assumes that you already know how to install a package and so you’ve got tidytext installed on your R machine. install.packages("tidytext") Loading the Library Let’s start with loading the tidytext library. library(tidytext) Extracting App Reviews We’ll use the R-package itunesr for downloading iOS App Reviews on which we’ll perform Simple Text Analysis (unigrams, bigrams, n-grams).

How to do negation-proof sentiment analysis in R

Sentiment Analysis is one of those things in Machine learning which is still getting improvement with the rise of Deep Learning based NLP solutions. There are many things like Sarcasm, Negations and similar items make Sentiment Analysis a rather tough nut to crack. Deep learning as much as it’s effective, it’s also computationally expensive and if you are ready to trade off between Cost (expense) and Accuracy, then you this is the solution for building a negation-proof Sentiment Analysis solution (in R).