library(tidyverse)
library(tidytext)
library(tm)
library(topicmodels)

glimpse(datsub)
Observations: 14,108
Variables: 6
$ Product.Name <chr> "iphone 4s", "iphone 4s", "iphone 4s", "iphone 4s", "iphone 4s", "iphone 4...
$ Brand.Name   <chr> "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""...
$ Price        <dbl> 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115,...
$ Rating       <int> 5, 1, 4, 5, 5, 3, 5, 5, 5, 1, 5, 5, 1, 5, 2, 5, 5, 4, 5, 1, 4, 1, 1, 1, 4,...
$ Reviews      <chr> "new great price phone really quick great seller", "star product false adv...
$ Review.Votes <int> 2, 1, 0, 1, 2, 2, 2, 5, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0,...

corpus = Corpus(VectorSource(datsub$Reviews))

dtm = DocumentTermMatrix(corpus)


ap_lda = LDA(dtm,
         method = "Gibbs",
         k = 4,
         control = list(seed = 1))

ap_topics = tidy(ap_lda, matrix = "beta")

ap_top_terms = ap_topics %>%
  group_by(topic) %>%
  top_n(5, beta) %>%
  arrange(topic, -beta)

ap_top_terms %>%
  ggplot(aes(reorder(term, beta), beta, fill = factor(topic))) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~ topic, scales = "free") +
  labs(x = "terms") +
  coord_flip()

cleaned_reviews = datsub %>%
  unnest_tokens(word, Reviews) %>%
  rename(term = word)

top_terms1 = ap_topics %>%
  filter(topic == 1) %>%
  arrange(-beta) %>%
  top_n(10, beta)

merged_dat1 =left_join(top_terms1, cleaned_reviews, by = "term") %>%
  group_by(Product.Name, term, beta) %>%
  summarise(n = n()) %>%
  group_by(Product.Name) %>%
  mutate(N = sum(n)) %>%
  group_by(Product.Name, term) %>%
  mutate(freq = n/N) %>%
  mutate(weight = freq * beta) %>%
  select(Product.Name, term, weight) %>%
  spread(term, weight)

glimpse(merged_dat1)
merged_dat1
# A tibble: 4 x 11
# Groups:   Product.Name [4]
  Product.Name       camera     fast  feature  good      love      low    nice  picture   price quality
  <chr>               <dbl>    <dbl>    <dbl> <dbl>     <dbl>    <dbl>   <dbl>    <dbl>   <dbl>   <dbl>
1 BLU Studio 5.0    0.00990 0.000747 0.000970 0.208 0.000201  0.000686 0.00428 0.00205  0.0115  0.00632
2 iphone 4s         0.00121 0.000626 0.000247 0.327 0.000204  0.000554 0.00625 0.000549 0.00588 0.00303
3 Motorola Moto E   0.0129  0.000573 0.00128  0.227 0.0000610 0.000981 0.00376 0.00162  0.0139  0.00451
4 Samsung Galaxy II 0.00520 0.000432 0.00150  0.277 0.000182  0.000541 0.00651 0.00117  0.00881 0.00305

dist1 = dist(merged_dat1, method = "euclidean", upper = TRUE, diag = TRUE)
    dist1 = as.matrix(dist1)

    dist2 = dist(merged_dat2, method = "euclidean", upper = TRUE, diag = TRUE)
    dist2 = as.matrix(dist2)

    mds1 = cmdscale(dist1, k= 1, eig = TRUE, x.ret = TRUE)
    mds2 = cmdscale(dist2, k= 1, eig = TRUE, x.ret = TRUE)

    dim1 = mds1$points
    dim2 = mds2$points


    data.frame(dim1, dim2) %>%
      ggplot() +
      geom_point(aes(x = dim1, y = dim2, color = merged_dat1$Product.Name)) +
      labs(x = "dim 1", y = "dim 1", color = "Brand") +
      theme_minimal()