Visualizing a Latent Dirichlet Allocation (LDA) by Multidimensional Scaling (MDS)

I did an LDA with four topics for four different Smartphones. This was done using customer Reviews of Amazon.

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

glimpse(datsub)
Observations: 14,108
Variables: 6
$$Product.Name  "iphone 4s", "iphone 4s", "iphone 4s", "iphone 4s", "iphone 4s", "iphone 4...$$ Brand.Name   <chr> "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", "", ""...
$$Price  115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115, 115,...$$ 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,...
$$Reviews  "new great price phone really quick great seller", "star product false adv...$$ 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,...


The result looks like this:

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()  The four topics represent the image dimensions of the four phones. Topic 1 could be named "Picture Quality", topic 2 "Battery", topic 3 "Features", and topic 4 "Satisfaction". I want to visualize the results on an MDS. The goal is to express how similar smartphones are regarding the four dimensions. My question is how this could be done? I have the following idea: Each term belongs to one of the four topics with a certain probability (beta). The bigger beta the more a term associated with a certain topic. The terms are distributed differently over all reviews regarding the four Smartphones. If I calculate the relative frequency of the terms of a certain topic and for a certain Smartphone and multiply the results with beta, I could express how strong a smartphone is associated with a topic. In R it would look like this: I take the top 10 terms (with the highest beta) of each topic, calculate the relative frequency of these terms per smartphone, multiply the relative frequency with beta (I would do this for all four topics). 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  Then I would compute the MDS (here for just for two topics, I would do this for all four topics)  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()


For all four topics the result looks like this:

The Problem is the results don´t make sense. The iphone has the highest satisfaction, but it´s less associated with the three other attributes wich leads to satisfaction. So something must be wrong. Any idea?