Last updated: 2024-08-10

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Knit directory: fastTopics-experiments/analysis/

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File Version Author Date Message
Rmd 7f54463 Peter Carbonetto 2024-08-10 workflowr::wflow_publish("newsgroups_more.Rmd", verbose = TRUE)
Rmd 8088af8 Peter Carbonetto 2024-08-08 Small fix to newsgroups_more.Rmd.
Rmd 47ce768 Peter Carbonetto 2024-08-08 Generated structure_plots_newsgroups.pdf.
html f79da59 Peter Carbonetto 2024-08-08 Added keywords to the newsgroups_more analysis.
Rmd 4c90df6 Peter Carbonetto 2024-08-08 workflowr::wflow_publish("newsgroups_more.Rmd", verbose = TRUE)
Rmd 7969f43 Peter Carbonetto 2024-08-07 Working on new ‘newsgroups_more’ analysis.
html a72103c Peter Carbonetto 2024-08-07 First build of the newsgroups_more analysis.
Rmd 269b84d Peter Carbonetto 2024-08-07 workflowr::wflow_publish("newsgroups_more.Rmd")

Here we take a closer look at some of the results on the newsgroups data.

Load the packages used in this analysis.

library(Matrix)
library(topicmodels)
library(fastTopics)
library(ggplot2)
library(cowplot)
set.seed(1)

Load the newsgroups data.

load("../data/newsgroups.RData")

Load the topic models fit using the EM and CD algorithms

fit1 <- readRDS("../output/newsgroups/rds/fit-newsgroups-em-k=10.rds")$fit
fit2 <- readRDS("../output/newsgroups/rds/fit-newsgroups-scd-ex-k=10.rds")$fit
fit1 <- poisson2multinom(fit1)
fit2 <- poisson2multinom(fit2)

and the LDA fits initialized using the EM and CD estimates:

lda1 <- readRDS("../output/newsgroups/rds/lda-newsgroups-em-k=10.rds")$lda
lda2 <- readRDS("../output/newsgroups/rds/lda-newsgroups-scd-ex-k=10.rds")$lda

The MLEs and the approximate posterior estimates from LDA turn out to be very similar to each other, so there is really no need to examine both. Here we’ll focus on the MLEs:

cor(as.vector(fit1$L),as.vector(lda1@gamma))
cor(as.vector(fit2$L),as.vector(lda2@gamma))
# [1] 0.9799571
# [1] 0.9790959

Let’s now examine the results using Structure plots. Here are the EM estimates:

n    <- nrow(fit1$L)
rows <- sample(n,2000)
L1   <- select_loadings(fit1,rows)$L
topics <- factor(topics,
                 c("rec.sport.hockey",
                   "rec.sport.baseball",
                   "sci.med",
                   "comp.graphics",
                   "comp.windows.x",
                   "comp.os.ms-windows.misc",
                   "comp.sys.ibm.pc.hardware",
                   "comp.sys.mac.hardware",
                   "misc.forsale",
                   "sci.electronics",
                   "sci.space",
                   "alt.atheism",
                   "soc.religion.christian",
                   "talk.religion.misc",
                   "rec.autos",
                   "rec.motorcycles",
                   "sci.crypt",
                   "talk.politics.misc",
                   "talk.politics.guns",
                   "talk.politics.mideast"))
topic_ordering <- c(2:10,1)
topic_colors <- c("#a6cee3","#1f78b4","#b2df8a","#33a02c","#fb9a99",
                  "#e31a1c","#fdbf6f","#ff7f00","#cab2d6","#6a3d9a")
p1 <- structure_plot(L1,topics = 1:10,grouping = topics[rows],
                     colors = topic_colors,gap = 20) +
  ggtitle("EM without extrapolation") +
  theme(plot.title = element_text(face = "plain",size = 10))
p1

Version Author Date
f79da59 Peter Carbonetto 2024-08-08

And here are the CD estimates:

L2 <- select_loadings(fit1,rows)$L
p2 <- structure_plot(L2,topics = 1:10,grouping = topics[rows],
                     colors = topic_colors,gap = 20) +
  ggtitle("CD with extrapolation") +
  theme(plot.title = element_text(face = "plain",size = 10))
p2

Version Author Date
f79da59 Peter Carbonetto 2024-08-08

The most striking differences are in topics 1 and 8.

Let’s now extract some “keywords” for a few selected topics by taking words that are at higher frequency in the given topic compared to the other topics. For example, top keywords for topic 9 clearly relate to baseball, hockey and sports more generally:

k <- 9
dat <- data.frame(word = colnames(counts),
                  f0 = apply(fit2$F[,-k],1,max),
                  f1 = fit1$F[,k],
                  f2 = fit2$F[,k])
subset(dat,f0 < 1e-5 & f2 > 1e-3)
#              word           f0           f1          f2
# baseball baseball 1.264878e-18 0.0020675252 0.002855391
# montreal montreal 7.251379e-06 0.0007823897 0.001078615
# bos           bos 1.264878e-18 0.0008482475 0.001169429
# players   players 9.595820e-06 0.0024795989 0.003422507
# hockey     hockey 1.264878e-18 0.0026976069 0.003719034
# det           det 1.264878e-18 0.0008797847 0.001276868
# braves     braves 1.264878e-18 0.0007343485 0.001012404
# playoffs playoffs 1.264878e-18 0.0007673193 0.001057858
# detroit   detroit 1.264878e-18 0.0009964893 0.001392571
# espn         espn 1.264878e-18 0.0008992023 0.001239678
# leafs       leafs 1.264878e-18 0.0007942954 0.001095049
# nhl           nhl 1.264878e-18 0.0012708726 0.001752078

The keywords for topic 1 seem to suggest a “background topic” that captures words that are not specific to any topic:

k <- 1
dat <- data.frame(word = colnames(counts),
                  f0 = apply(fit2$F[,-k],1,max),
                  f1 = fit1$F[,k],
                  f2 = fit2$F[,k])
subset(dat,f0 > 1e-6 & f2/f0 > 5)
#              word           f0           f1           f2
# sure         sure 2.512762e-04 1.090897e-03 1.692536e-03
# just         just 1.076207e-03 4.551963e-03 5.842350e-03
# keeps       keeps 1.817499e-05 6.700066e-05 1.027829e-04
# don           don 6.465096e-04 3.991194e-03 6.548491e-03
# anyway     anyway 1.175301e-04 6.365821e-04 7.084094e-04
# nope         nope 1.028566e-05 3.561286e-05 5.508114e-05
# happens   happens 4.295433e-05 2.128200e-04 3.140891e-04
# wouldn     wouldn 5.921440e-05 5.217093e-04 7.396585e-04
# going       going 1.934898e-04 1.441073e-03 2.156136e-03
# really     really 2.526422e-04 1.844767e-03 2.443492e-03
# shouldn   shouldn 3.461313e-05 1.453972e-04 2.660836e-04
# maybe       maybe 1.235100e-04 8.788490e-04 1.177699e-03
# guess       guess 6.037515e-05 4.870811e-04 7.643763e-04
# worse       worse 3.896588e-05 1.794090e-04 3.210723e-04
# glad         glad 1.560221e-05 9.443811e-05 1.257976e-04
# lot           lot 2.316579e-04 9.727162e-04 1.324099e-03
# complain complain 7.588646e-06 7.343811e-05 8.735658e-05
# aren         aren 9.011923e-05 3.236008e-04 4.946874e-04
# wasting   wasting 9.404078e-06 4.273874e-05 4.860590e-05
# bothered bothered 5.526302e-06 2.487617e-05 5.460924e-05
# fucking   fucking 1.700176e-06 1.568006e-05 3.497150e-05
# stupid     stupid 6.411798e-05 2.708437e-04 3.229429e-04
# scary       scary 5.386777e-06 3.737996e-05 4.461554e-05
# squashed squashed 1.063293e-06 9.245623e-06 6.194854e-06
# sounded   sounded 7.305338e-06 5.979282e-05 3.832783e-05
# hiking     hiking 1.717477e-06 2.150255e-05 9.142511e-06

Finally, topic 8 is a topic that is quite noticeably different between the EM and CD estimates, and indeed based on the keywords, only the CD estimates produce a topic about cars and motorcycles, with keywords such as wheel, riding, bmw, etc:

k <- 8
dat <- data.frame(word = colnames(counts),
                  f0 = apply(fit2$F[,-k],1,max),
                  f1 = fit1$F[,k],
                  f2 = fit2$F[,k])
subset(dat,f0 < 1e-5 & f2 > 5e-4)
#                    word           f0           f1           f2
# wheel             wheel 6.543365e-06 1.683374e-18 0.0009816094
# bmw                 bmw 1.264878e-18 3.192401e-18 0.0015759252
# mustang         mustang 1.264878e-18 1.330000e-18 0.0005921106
# ford               ford 9.643654e-06 1.371222e-04 0.0013342849
# helmet           helmet 9.667809e-06 1.337740e-18 0.0008102502
# di                   di 1.812754e-06 8.805320e-04 0.0007696219
# mov                 mov 1.264878e-18 7.427849e-04 0.0006422123
# cx                   cx 7.585213e-06 6.729076e-04 0.0005761026
# ei                   ei 1.264878e-18 8.218046e-04 0.0007105328
# bike               bike 1.264878e-18 5.693028e-18 0.0038122818
# toyota           toyota 1.264878e-18 1.330000e-18 0.0005875557
# tire               tire 8.870782e-06 1.330000e-18 0.0005369584
# honda             honda 1.264878e-18 3.048671e-18 0.0010657990
# brakes           brakes 1.264878e-18 1.330000e-18 0.0005192353
# brake             brake 5.320512e-06 1.330000e-18 0.0007191107
# tires             tires 4.859009e-06 1.330000e-18 0.0007878724
# callison       callison 1.264878e-18 1.330000e-18 0.0005010166
# bikes             bikes 1.264878e-18 1.483688e-18 0.0008972752
# motorcycles motorcycles 1.264878e-18 1.330000e-18 0.0006285481
# behanna         behanna 1.264878e-18 1.330000e-18 0.0005328995

sessionInfo()
# R version 4.3.3 (2024-02-29)
# Platform: aarch64-apple-darwin20 (64-bit)
# Running under: macOS Sonoma 14.5
# 
# Matrix products: default
# BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
# LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0
# 
# locale:
# [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
# 
# time zone: America/Chicago
# tzcode source: internal
# 
# attached base packages:
# [1] stats     graphics  grDevices utils     datasets  methods   base     
# 
# other attached packages:
# [1] cowplot_1.1.3      ggplot2_3.5.0      fastTopics_0.6-184 topicmodels_0.2-16
# [5] Matrix_1.6-5      
# 
# loaded via a namespace (and not attached):
#  [1] gtable_0.3.4        xfun_0.42           bslib_0.6.1        
#  [4] htmlwidgets_1.6.4   ggrepel_0.9.5       lattice_0.22-5     
#  [7] quadprog_1.5-8      vctrs_0.6.5         tools_4.3.3        
# [10] generics_0.1.3      stats4_4.3.3        parallel_4.3.3     
# [13] tibble_3.2.1        fansi_1.0.6         highr_0.10         
# [16] pkgconfig_2.0.3     data.table_1.15.2   SQUAREM_2021.1     
# [19] RcppParallel_5.1.7  lifecycle_1.0.4     truncnorm_1.0-9    
# [22] farver_2.1.1        compiler_4.3.3      stringr_1.5.1      
# [25] git2r_0.33.0        textshaping_0.3.7   progress_1.2.3     
# [28] munsell_0.5.0       RhpcBLASctl_0.23-42 httpuv_1.6.14      
# [31] htmltools_0.5.7     sass_0.4.8          lazyeval_0.2.2     
# [34] yaml_2.3.8          plotly_4.10.4       crayon_1.5.2       
# [37] tidyr_1.3.1         later_1.3.2         pillar_1.9.0       
# [40] jquerylib_0.1.4     whisker_0.4.1       uwot_0.1.16        
# [43] cachem_1.0.8        gtools_3.9.5        tidyselect_1.2.1   
# [46] digest_0.6.34       Rtsne_0.17          stringi_1.8.3      
# [49] slam_0.1-50         purrr_1.0.2         dplyr_1.1.4        
# [52] ashr_2.2-66         labeling_0.4.3      rprojroot_2.0.4    
# [55] fastmap_1.1.1       grid_4.3.3          colorspace_2.1-0   
# [58] cli_3.6.2           invgamma_1.1        magrittr_2.0.3     
# [61] utf8_1.2.4          withr_3.0.0         prettyunits_1.2.0  
# [64] scales_1.3.0        promises_1.2.1      rmarkdown_2.26     
# [67] httr_1.4.7          workflowr_1.7.1     ragg_1.2.7         
# [70] hms_1.1.3           modeltools_0.2-23   NLP_0.2-1          
# [73] pbapply_1.7-2       evaluate_0.23       knitr_1.45         
# [76] viridisLite_0.4.2   irlba_2.3.5.1       tm_0.7-13          
# [79] rlang_1.1.3         Rcpp_1.0.12         mixsqp_0.3-54      
# [82] glue_1.7.0          xml2_1.3.6          jsonlite_1.8.8     
# [85] R6_2.5.1            systemfonts_1.0.6   fs_1.6.3