A closer look at some results on the newsgroups data
Peter Carbonetto
Last updated: 2024-08-10
Checks: 7 0
Knit directory:
fastTopics-experiments/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 1716ea3 | Peter Carbonetto | 2024-08-10 | workflowr::wflow_publish("newsgroups_more.Rmd", verbose = TRUE) |
| html | 2b1b3d5 | Peter Carbonetto | 2024-08-10 | Switching to MLEs in newsgroups_more. |
| 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")$lda
lda2 <- readRDS("../output/newsgroups/rds/lda-newsgroups-scd-ex-k=10.rds")$ldaThe 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] 0.9790959Let’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
And here are the CD estimates:
L2 <- select_loadings(fit2,rows)$L
p2 <- structure_plot(L2,topics = 1:10,grouping = topics[rows],
colors = topic_colors,gap = 20) +
ggtitle("CD with extrapolation") +
theme(plot.title = element_text(face = "plain",size = 10))
p2
The most striking differences are in topics 1 and 8.
Let’s now extract some “keywords” for a few selected topics by taking words that are at higher frequency in the given topic compared to the other topics. For example, top keywords for topic 9 clearly relate to baseball, hockey and sports more generally:
k <- 9
dat <- data.frame(word = colnames(counts),
f0 = apply(fit2$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 0.001752078The 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$F[,k])
subset(dat,f0 > 1e-6 & f2/f0 > 5)
# word f0 f1 f2
# sure sure 2.512762e-04 1.090897e-03 1.692536e-03
# just just 1.076207e-03 4.551963e-03 5.842350e-03
# keeps keeps 1.817499e-05 6.700066e-05 1.027829e-04
# don don 6.465096e-04 3.991194e-03 6.548491e-03
# anyway anyway 1.175301e-04 6.365821e-04 7.084094e-04
# nope nope 1.028566e-05 3.561286e-05 5.508114e-05
# happens happens 4.295433e-05 2.128200e-04 3.140891e-04
# wouldn wouldn 5.921440e-05 5.217093e-04 7.396585e-04
# going going 1.934898e-04 1.441073e-03 2.156136e-03
# really really 2.526422e-04 1.844767e-03 2.443492e-03
# shouldn shouldn 3.461313e-05 1.453972e-04 2.660836e-04
# maybe maybe 1.235100e-04 8.788490e-04 1.177699e-03
# guess guess 6.037515e-05 4.870811e-04 7.643763e-04
# worse worse 3.896588e-05 1.794090e-04 3.210723e-04
# glad glad 1.560221e-05 9.443811e-05 1.257976e-04
# lot lot 2.316579e-04 9.727162e-04 1.324099e-03
# complain complain 7.588646e-06 7.343811e-05 8.735658e-05
# aren aren 9.011923e-05 3.236008e-04 4.946874e-04
# wasting wasting 9.404078e-06 4.273874e-05 4.860590e-05
# bothered bothered 5.526302e-06 2.487617e-05 5.460924e-05
# fucking fucking 1.700176e-06 1.568006e-05 3.497150e-05
# stupid stupid 6.411798e-05 2.708437e-04 3.229429e-04
# scary scary 5.386777e-06 3.737996e-05 4.461554e-05
# squashed squashed 1.063293e-06 9.245623e-06 6.194854e-06
# sounded sounded 7.305338e-06 5.979282e-05 3.832783e-05
# hiking hiking 1.717477e-06 2.150255e-05 9.142511e-06Finally, topic 8 is a topic that is quite noticeably different between the EM and CD estimates, and indeed based on the keywords, only the CD estimates produce a topic about cars and motorcycles, with keywords such as wheel, riding, bmw, etc:
k <- 8
dat <- data.frame(word = colnames(counts),
f0 = apply(fit2$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