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topic.modelling.R
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topic.modelling.R
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# Author: SAMUEL ABOYE
# Title: Comcast Telecom Consumer Complaints.
# Date: Feb 2021
library(tm)
library(ggplot2)
library(readr)
library(wordcloud)
library(RColorBrewer)
library(plyr)
library(topicmodels)
require(Snowballc)
# Step 2: load the dataset
data <- read.csv("dataset_comcast/Comcast_Telecom_Complaints_data.csv")
text <- data$Customer.Complaint
View(text)
text <- as.character(text)
sample <- sample(text, (length(text)))
corpus <- Corpus(VectorSource(list(sample)))
corpus <- tm_map(corpus, removePunctuation)
corpus <- tm_map(corpus, content_transformer(tolower))
corpus <- tm_map(corpus, removeNumbers)
corpus <- tm_map(corpus, removeWords, c(stopwords('english'),'comcast'))
dtm <- DocumentTermMatrix(VCorpus(VectorSource(corpus[[1]]$content)))
dtm
lda <- LDA(dtm, k=4, control = list(seed=500))
lda
library(tidytext)
topics <- tidy(lda, matrix= "beta")
topics
library(dplyr)
topic_term <- topics %>%
group_by(topic) %>%
top_n(10, beta) %>%
ungroup() %>%
arrange(topic, -beta)
view(topic)
topic_term %>%
mutate(term= reorder(term,beta)) %>%
ggplot(aes(term, beta, fill= factor(topic))) +
geom_col(show.legend = FALSE) +
facet_wrap( ~ topic, scales = "free") +
coord_flip()