Predict sentiment score using multiclass logistic regression with R - regression

Question

I am trying to create a sentiment analysis classifier using logistic regression with R (glmnet).. Here is the R code :
library(tidyverse)
library(text2vec)
library(caret)
library(glmnet)
library(ggrepel)
Train_classifier <- read.csv('IRC.csv',header=T, sep=";")
Test_classifier <- read.csv('IRC2.csv',header=T, sep=";")
# select only 4 column of the dataframe
Train <- Train_classifier[, c("Note.Reco", "Raison.Reco", "DATE_SAISIE", "idpart")]
Test <- Test_classifier[, c("Note.Reco", "Raison.Reco", "DATE_SAISIE", "idpart")]
#delete rows with empty value columns
subTrain <- filter(Train, trimws(Raison.Reco)!=" ")
subTrain$ID <- seq.int(nrow(subTrain))
# # replacing class values
subTrain$Note.Reco = ifelse(subTrain$Note.Reco >= 0 & subTrain$Note.Reco <= 4, 0, ifelse(subTrain$Note.Reco >= 5 &
subTrain$Note.Reco <= 6, 1, ifelse(subTrain$Note.Reco >= 7 & subTrain$Note.Reco <= 8, 2, 3)))
subTest <- filter(Test, trimws(Raison.Reco)!=" ")
subTest$ID <- seq.int(nrow(subTest))
#Data pre processing
#Doc2Vec
prep_fun <- tolower
tok_fun <- word_tokenizer
subTrain[] <- lapply(subTrain, as.character)
it_train <- itoken(subTrain$Raison.Reco,
preprocessor = prep_fun,
tokenizer = tok_fun,
ids = subTrain$ID,
progressbar = TRUE)
subTest[] <- lapply(subTest, as.character)
it_test <- itoken(subTest$Raison.Reco,
preprocessor = prep_fun,
tokenizer = tok_fun,
ids = subTest$ID,
progressbar = TRUE)
#creation of vocabulairy and term document matrix
### fichier d'apprentissage
vocab_train <- create_vocabulary(it_train)
vectorizer_train <- vocab_vectorizer(vocab_train)
dtm_train <- create_dtm(it_train, vectorizer)
### test data
vocab_test <- create_vocabulary(it_test)
vectorizer_test <- vocab_vectorizer(vocab_test)
dtm_test <- create_dtm(it_test, vectorizer_test)
##Define tf-idf model
tfidf <- TfIdf$new()
# fit the model to the train data and transform it with the fitted model
dtm_train_tfidf <- fit_transform(dtm_train, tfidf)
dtm_test_tfidf <- fit_transform(dtm_test, tfidf)
glmnet_classifier <- cv.glmnet(x = dtm_train_tfidf,
y = subTrain[['Note.Reco']],
family = 'multinomial',
# L1 penalty
alpha = 1,
# interested in the area under ROC curve
type.measure = "auc",
# 5-fold cross-validation
nfolds = 5,
# high value is less accurate, but has faster training
thresh = 1e-3,
# again lower number of iterations for faster training
maxit = 1e3)
plot(glmnet_classifier)
Here is the struct of the data subTrain :
[![Note.Reco Raison.Reco DATE_SAISIE idpart ID
3 Good service 19/03/2014 56992
2 good stuff 19/03/2014 53645
8 very nice 20/02/2016 261392
...][1]][1]
I get this plot (attached file) Can you explain me more if it is true Thank you