EmoReader is a R package that generates the sentiments from the text document. It generates 10 different emotion from the text document and all the emotions are scored accordingly which is then coverted to a dgc matrix for the dimension reduction and clustering.
We need to first install the devtools package. You can install the devtools using the below code:
install.packages("devtools")
You would need to install the singlet package that will be used to run the nmf_func in the EmoReader package. You can install the singlet using the below code:
library(devtools)
devtools::install_github("zdebruine/singlet")
library(singlet)
You can install the EmoReader package from the GitHub:
library(devtools)
devtools::install_github("dikshashrestha/EmoReader")
library (EmoReader)
The link to the pkgdown site: https://dikshashrestha.github.io/EmoReader/
The link to the package vignette is below: https://dikshashrestha.github.io/EmoReader/articles/amazon_vig.html
df <- read_inbuilt_data(amazon_data)
str(df)
# 'data.frame': 500 obs. of 2 variables:
# $ star_rating: int 5 5 5 4 2 3 3 5 4 5 ...
# $ review : chr "As advertised. Everything works perfectly, I'm very happy with the camera. As a matter of fact I'm going to buy"| __truncated__ # # "it's great" "These work great and fit my life proof case for the iPhone 6" "Work very well but could not get used to not hearing anything out of the # ear they v were plugged into." ...
my_mat <-matrix_conversion(emo_mat)
head(my_mat)
# anger anticipation disgust fear joy sadness surprise trust negative positive
# [1,] 0 1 0 0 1 0 0 2 0 1
# [2,] 0 0 0 0 0 0 0 0 0 0
# [3,] 0 0 0 1 0 1 0 1 1 0
# [4,] 0 0 0 1 0 0 0 0 1 0
# [5,] 1 1 0 0 1 0 0 1 1 3
sparse_mat <- convert_to_sparse_matrix()
head(sparse_mat)
# 6 x 10 sparse Matrix of class "dgCMatrix"
# [[ suppressing 10 column names ‘anger’, ‘anticipation’, ‘disgust’ ... ]]
# [1,] . 1 . . 1 . . 2 . 1
# [2,] . . . . . . . . . .
# [3,] . . . 1 . 1 . 1 1 .
# [4,] . . . 1 . . . . 1 .
# [5,] 1 1 . . 1 . . 1 1 3
# [6,] . 1 . . 1 . 1 1 . 3
The below function reads the csv file and it is then passed to a dataframe afte renaming the columns.
library(readr)
data <- read.csv('Flipkart_Customer_Review.csv')
data <- data.frame(star_rating = data$rating, review = data$review)
Our read_inbuilt_data function then pre-process the dataframe which is passed to the data.
df_flip <- read_inbuilt_data(data)
str(df_flip)
# 'data.frame': 1000 obs. of 2 variables:
# $ star_rating: int 5 5 4 5 5 5 4 4 5 5 ...
# $ review : chr "It was nice produt. I like it's design a lot. It's easy to carry. And. Looked stylish.READ MORE" "awesome sound....very pretty # to see this nd the sound quality was too good I wish to take this product loved ...
emo_mat <- get_emotion(df_flip)
head(emo_mat)
# anger anticipation disgust fear joy sadness surprise trust negative positive
# 1 0 0 0 0 0 0 0 0 0 0
# 2 0 2 0 0 2 0 1 2 0 2
# 3 1 3 0 1 1 0 1 3 1 4
# 4 0 2 0 1 3 1 1 4 2 5
# 5 0 2 0 0 1 0 1 1 1 2
# 6 2 4 1 1 5 1 0 4 1 6