-
Notifications
You must be signed in to change notification settings - Fork 53
/
README.Rmd
371 lines (276 loc) · 20.9 KB
/
README.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
---
title: "Topic Models Learning and R Resources"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
md_document:
toc: true
toc_depth: 2
---
```{r, echo=FALSE, message=FALSE}
# rmarkdown::render("README.Rmd", "all"); md_toc()
library(knitr)
knit_hooks$set(htmlcap = function(before, options, envir) {
if(!before) {
paste('<p class="caption"><b><em>',options$htmlcap,"</em></b></p>",sep="")
}
})
knitr::opts_knit$set(self.contained = TRUE, cache = FALSE)
knitr::opts_chunk$set(fig.path = "inst/figure/")
```
This is a collection documenting the resources I find related to topic models with an R flavored focus. A *topic model* is a type of [*generative*](http://stackoverflow.com/questions/879432/what-is-the-difference-between-a-generative-and-discriminative-algorithm) model used to "discover" latent topics that compose a *corpus* or collection of documents. Typically topic modeling is used on a collection of text documents but can be used for other modes including use as caption generation for images.
# Just the Essentials
This is my run down of the minimal readings, websites, videos, & scripts the reader needs to become familiar with topic modeling. The list is in an order I believe will be of greatest use and contains a nice mix of introduction, theory, application, and interpretation. As you want to learn more about topic modeling, the other sections will become more useful.
1. Boyd-Graber, J. (2013). [Computational Linguistics I: Topic Modeling](https://www.youtube.com/watch?v=4p9MSJy761Y)
2. Underwood, T. (2012). [Topic Modeling Made Just Simple Enough](http://tedunderwood.com/2012/04/07/topic-modeling-made-just-simple-enough/)
3. Weingart, S. (2012). [Topic Modeling for Humanists: A Guided Tour](http://www.scottbot.net/HIAL/?p=19113)
4. Blei, D. M. (2012). [Probabilistic topic models](/articles/Blei2012.pdf). *Communications of the ACM, (55)*4, 77-84. doi:10.1145/2133806.2133826
5. inkhorn82 (2014). [A Delicious Analysis! (aka topic modelling using recipes)](http://rforwork.info/2014/02/17/a-delicious-analysis/) [(CODE)](https://gist.githubusercontent.com/inkhorn/9044779/raw/c7f0ba30d424aaeb75c5e221d12566f6732c4f29/recipe%20analysis.R)
6. Grüen, B. & Hornik, K. (2011). [topicmodels: An R Package for Fitting Topic Models.](/articles/Gruen2011.pdf). *Journal of Statistical Software, 40*(13), 1-30.
7. Marwick, B. (2014a). [The input parameters for using latent Dirichlet allocation](http://stats.stackexchange.com/a/25128/7482)
8. Tang, J., Meng, Z., Nguyen, X. , Mei, Q. , & Zhang, M. (2014). [Understanding the limiting factors of topic modeling via posterior contraction analysis](/articles/Tang2014.pdf). In *31 st International Conference on Machine Learning*, 190-198.
9. Sievert, C. (2014). [LDAvis: A method for visualizing and interpreting topic models](https://www.youtube.com/watch?v=IksL96ls4o0)
10. Rhody, L. M. (2012). [Some Assembly Required: Understanding and Interpreting Topics in LDA Models of Figurative Language](http://www.lisarhody.com/some-assembly-required)
11. Rinker, T.W. (2015). [R Script: Example Topic Model Analysis](https://raw.githubusercontent.com/trinker/topicmodels_learning/master/scripts/Example_topic_model_analysis.R)
# Key Players
Papadimitriou, Raghavan, Tamaki & Vempala, Santosh (1997) first introduced the notion of topic modeling in their ["Latent Semantic Indexing: A probabilistic analysis"](/articles/Papadimitriou1997.pdf). Thomas Hofmann (1999) developed "Probabilistic latent semantic indexing". Blei, Ng, & Jordan (2003) proposed *latent Dirichlet allocation* (LDA) as a means of modeling documents with multiple topics but assumes the topic are uncorrelated. Blei & Lafferty (2007) proposed *correlated topics model* (CTM), extending LDA to allow for correlations between topics. Roberts, Stewart, Tingley, & Airoldi (2013) propose a [*Structural Topic Model*](/articles/Roberts2013.pdf) (STM), allowing the inclusion of meta-data in the modeling process.
# Videos
## Introductory
- Boyd-Graber, J. (2013). [Computational Linguistics I: Topic Modeling](https://www.youtube.com/watch?v=4p9MSJy761Y)
## Theory
- Blei, D. (2007) [Modeling Science: Dynamic Topic Models of Scholarly Research](https://www.youtube.com/watch?v=7BMsuyBPx90)
- Blei, D. (2009) [Topic Models: Parts I & II](http://videolectures.net/mlss09uk_blei_tm/#) ([Lecture Notes](/presentations/Blei2009.pdf))
- Jordan, M. (2014) [A Short History of Topic Models](https://www.youtube.com/watch?v=fBNsHPtTAGs)
## Visualization
- Sievert, C. (2014) [LDAvis: A method for visualizing and interpreting topic models](https://www.youtube.com/watch?v=IksL96ls4o0)
- Maybe, B. (2015) [SavvySharpa: Visualizing Topic Models](https://www.youtube.com/watch?v=tGxW2BzC_DU)
# Articles
## Applied
- Marwick, B. 2013. [Discovery of Emergent Issues and Controversies in Anthropology Using Text Mining, Topic Modeling, and Social Network Analysis of Microblog Content](https://www.academia.edu/5508141/Discovery_of_Emergent_Issues_and_Controversies_in_Anthropology_Using_Text_Mining_Topic_Modeling_and_Social_Network_Analysis_of_Microblog_Content). In Yanchang Zhao, Yonghua Cen (eds) Data Mining Applications with R. Elsevier. p. 63-93
- Newman, D.J. & Block, S. (2006). [Probabilistic topic decomposition of an eighteenth-century American newspaper](/articles/Newman2006.pdf). *Journal of the American Society for Information Science and Technology. 57*(6), 753-767. doi:10.1002/asi.v57:6
## Theoretical
- Blei, D. M. (2012). [Probabilistic topic models](/articles/Blei2012.pdf). *Communications of the ACM, (55)*4, 77-84. doi:10.1145/2133806.2133826
- Blei, D. M. & Lafferty, J. D. (2007) [A correlated topic model of Science](/articles/Blei2007.pdf). *The Annals of Applied Statistics 1*(1), 17-35. doi:10.1214/07-AOAS114
- Blei, D. M. & Lafferty, J. D. (2009) [Topic models](/articles/Blei2009.pdf). In A Srivastava, M Sahami (eds.), [*Text mining: classification, clustering, and applications*](/articles/Srivastava2009.pdf). Chapman & Hall/CRC Press. 71-93.
- Blei, D. M. & McAuliffe, J. (2008). [Supervised topic models](/articles/Blei2008.pdf). In Advances in Neural Information Processing Systems 20, 1-8.
- Blei, D. M., Ng, A.Y., & Jordan, M.I. (2003). [Latent Dirichlet Allocation](/articles/Blei2003.pdf). *Journal of Machine Learning Research, 3*, 993-1022.
- Chang, J., Boyd-Graber, J. , Wang, C., Gerrish, S., & Blei. D. (2009). [Reading tea leaves: How humans interpret topic models](/articles/Chang2009.pdf). In *Neural Information Processing Systems*.
- Griffiths, T.L. & Steyvers, M. (2004). [Finding Scientific Topics](/articles/Griffiths2004.pdf). Proceedings of the National
Academy of Sciences of the United States of America, 101, 5228-5235.
- Griffiths, T.L., Steyvers, M., & Tenenbaum, J.B.T. (2007). [Topics in Semantic Representation](/articles/Griffiths2007.pdf). *Psychological Review, 114*(2), 211-244.
- Grüen, B. & Hornik, K. (2011). [topicmodels: An R Package for Fitting Topic Models.](/articles/Gruen2011.pdf). *Journal of Statistical Software, 40*(13), 1-30.
- Mimno, D. & A. Mccallum. (2007). [Organizing the OCA: learning faceted subjects from a library of digital books](/articles/Mimno2007.pdf). In *Joint Conference on Digital Libraries*. ACM Press, New York, NY, 376–385.
- Ponweiser, M. (2012). [Latent Dirichlet Allocation in R (Diploma Thesis)](/articles/Ponweiser2012.pdf). Vienna University of Economics and Business, Vienna
- Roberts M.E., Stewart B.M., Tingley D., & Airoldi E.M. (2013) [The Structural Topic Model and Applied Social Science](/articles/Roberts2013.pdf). *Advances in Neural Information Processing Systems Workshop on Topic Models: Computation, Application, and Evaluation*, 1-4.
- Roberts, M., Stewart, B., Tingley, D., Lucas, C., Leder-Luis, J., Gadarian, S., Albertson, B., et al. (2014). [Structural topic models for open ended survey responses](/articles/Roberts2014.pdf). *American Journal of Political Science, American Journal of Political Science, 58*(4), 1064-1082.
- Roberts, M., Stewart, B., Tingley, D. (n.d.). [stm: R Package for Structural Topic Models](/articles/Robertsnd.pdf), 1-49.
- Sievert, C. & Shirley, K. E. (2014a). [LDAvis: A Method for Visualizing and Interpreting Topics.](/articles/Sievert2014a.pdf) in *Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces* 63-70.
- Steyvers, M. & Griffiths, T. (2007). [Probabilistic topic models](/articles/Steyvers2007.pdf). In T. Landauer, D McNamara, S. Dennis, and W. Kintsch (eds), *Latent Semantic Analysis: A Road to Meaning*. Laurence Erlbaum
- Taddy, M.A. (2012). [On Estimation and Selection for Topic Models](/articles/Taddy2012.pdf) In *Proceedings of the 15th International Conference on Artificial Intelligence and Statistics (AISTATS 2012)*, 1184-1193.
- Tang, J., Meng, Z., Nguyen, X. , Mei, Q. , & Zhang, M. (2014). [Understanding the limiting factors of topic modeling via posterior contraction analysis](/articles/Tang2014.pdf). In *31 st International Conference on Machine Learning*, 190-198.
# Websites & Blogs
- Blei, D. (n.d.). [Topic Modeling](https://www.cs.princeton.edu/~blei/topicmodeling.html)
- Jockers, M.L. (2013). ["Secret" Recipe for Topic Modeling Themes](http://www.matthewjockers.net/2013/04/12/secret-recipe-for-topic-modeling-themes/)
- Jones, T. (n.d.). [Topic Models Reading List](http://www.biasedestimates.com/p/topic-models-reading-list.html)
- Marwick, B. (2014a). [The input parameters for using latent Dirichlet allocation](http://stats.stackexchange.com/a/25128/7482)
- Marwick, B. (2014b). [Topic models: cross validation with loglikelihood or perplexity](http://stackoverflow.com/a/21394092/1000343)
- Rhody, L. M. (2012). [Some Assembly Required: Understanding and Interpreting Topics in LDA Models of Figurative Language](http://www.lisarhody.com/some-assembly-required)
- Schmidt, B.M. (2012). [Words Alone: Dismantling Topic Models in the Humanities](http://journalofdigitalhumanities.org/2-1/words-alone-by-benjamin-m-schmidt/)
- Underwood, T. (2012a). [Topic Modeling Made Just Simple Enough](http://tedunderwood.com/2012/04/07/topic-modeling-made-just-simple-enough/)
- Underwood, T. (2012b). [What kinds of "topics" does topic modeling actually produce?](http://tedunderwood.com/2012/04/01/what-kinds-of-topics-does-topic-modeling-actually-produce/)
- Weingart, S. (2012). [Topic Modeling for Humanists: A Guided Tour](http://www.scottbot.net/HIAL/?p=19113)
- Weingart, S. (2011). [Topic Modeling and Network Analysis](http://www.scottbot.net/HIAL/?p=221)
# R Resources
## Package Comparisons
| Package | Functionality | Pluses | Author | R Language Interface |
|-------------- | -------------|---------|----------|---------------------|
| lda* | Collapsed Gibbs for LDA | Graphing utilities | Chang | R |
| topicmodels | LDA and CTM | Follows Blei's implementation; great vignette; takes | C | [DTM](https://en.wikipedia.org/wiki/Document-term_matrix) | Grüen & Hornik |
| stm | Model w/ meta-data | Great documentation; nice visualization | Roberts, Stewart, & Tingley | C |
| LDAvis | Interactive visualization | Aids in model interpretation | Sievert & Shirley | R + Shiny |
| mallet** | LDA | [MALLET](http://programminghistorian.org/lessons/topic-modeling-and-mallet) is well known | Mimno | Java |
\*[*StackExchange discussion of lda vs. topicmodels*](http://stats.stackexchange.com/questions/24441/two-r-packages-for-topic-modeling-lda-and-topicmodels)
\*\*[*Setting Up MALLET*](http://programminghistorian.org/lessons/topic-modeling-and-mallet)
## R Specific References
- Chang J. (2010). lda: Collapsed Gibbs Sampling Methods for Topic Models. http://CRAN.R-project.org/package=lda.
- Grüen, B. & Hornik, K. (2011). [topicmodels: An R Package for Fitting Topic Models.](/articles/Gruen2011.pdf). *Journal of Statistical Software, 40*(13), 1-30.
- Mimno, D. (2013). [vignette-mallet: A wrapper around the Java machine learning tool MALLET](/articles/Mimno2013.Rmd). https://CRAN.R-project.org/package=mallet
- Ponweiser, M. (2012). [Latent Dirichlet Allocation in R (Diploma Thesis)](/articles/Ponweiser2012.pdf). Vienna University of Economics and Business, Vienna.
- Roberts, M., Stewart, B., Tingley, D. (n.d.). [stm: R Package for Structural Topic Models](/articles/Robertsnd.pdf), 1-49.
- Sievert, C. & Shirley, K. E. (2014a). [LDAvis: A Method for Visualizing and Interpreting Topics.](Sievert2014a.pdf) *Proceedings of the Workshop on Interactive Language Learning, Visualization, and Interfaces* 63-70.
- Sievert, C. & Shirley, K. E. (2014b). [Vignette: LDAvis details.](/articles/Sievert2014b.pdf) 1-5.
## Example Modeling
- Awati, K. (2015). [A gentle introduction to topic modeling using R](https://eight2late.wordpress.com/2015/09/29/a-gentle-introduction-to-topic-modeling-using-r/)
- Dubins, M. (2013). [Topic Modeling in Python and R: A Rather Nosy Analysis of the Enron Email Corpus](https://dzone.com/articles/topic-modeling-python-and-r)
- Goodrich, B. (2015) [Topic Modeling Twitter Using R](https://www.linkedin.com/pulse/topic-modeling-twitter-using-r-bryan-goodrich) [(CODE)](https://gist.githubusercontent.com/bryangoodrich/7b5ef683ce8db592669e/raw/3402e7390d10a0282dc0d6309ed4df9a4fb1cf5d/TwitterTopics.r)
- inkhorn82 (2014). [A Delicious Analysis! (aka topic modelling using recipes)](http://rforwork.info/2014/02/17/a-delicious-analysis/) [(CODE)](https://gist.githubusercontent.com/inkhorn/9044779/raw/c7f0ba30d424aaeb75c5e221d12566f6732c4f29/recipe%20analysis.R)
- Jockers, M.L. (2014).[Introduction to Text Analysis and Topic Modeling with R](http://www.matthewjockers.net/materials/dh-2014-introduction-to-text-analysis-and-topic-modeling-with-r/)
- Medina, L. (2015). [Conspiracy Theories - Topic Modeling & Keyword Extraction](http://voidpatterns.org/2015/03/conspiracy-theories-topic-modeling-keyword-extraction/)
- Sievert, C. (n.d.). [A topic model for movie reviews](http://cpsievert.github.io/LDAvis/reviews/reviews.html)
- Sievert, C. (2014). [Topic Modeling In R](https://ropensci.org/blog/2014/04/16/topic-modeling-in-R/)
# Topic Modeling R Demo
## topicmodels Package
The .R script for this demonstration can be downloaded from [scripts/Example_topic_model_analysis.R](https://raw.githubusercontent.com/trinker/topicmodels_learning/master/scripts/Example_topic_model_analysis.R)
### Install/Load Tools & Data
```{r}
if (!require("pacman")) install.packages("pacman")
pacman::p_load_gh("trinker/gofastr")
pacman::p_load(tm, topicmodels, dplyr, tidyr, igraph, devtools, LDAvis, ggplot2)
## Source topicmodels2LDAvis & optimal_k functions
invisible(lapply(
file.path(
"https://raw.githubusercontent.com/trinker/topicmodels_learning/master/functions",
c("topicmodels2LDAvis.R", "optimal_k.R")
),
devtools::source_url
))
data(presidential_debates_2012)
```
### Generate Stopwords
```{r}
stops <- c(
tm::stopwords("english"),
tm::stopwords("SMART"),
"governor", "president", "mister", "obama","romney"
) %>%
gofastr::prep_stopwords()
```
### Create the DocumentTermMatrix
```{r}
doc_term_mat <- presidential_debates_2012 %>%
with(gofastr::q_dtm_stem(dialogue, paste(person, time, sep = "_"))) %>%
gofastr::remove_stopwords(stops, stem=TRUE) %>%
gofastr::filter_tf_idf() %>%
gofastr::filter_documents()
```
### Control List
```{r}
control <- list(burnin = 500, iter = 1000, keep = 100, seed = 2500)
```
### Determine Optimal Number of Topics
The plot below shows the harmonic mean of the log likelihoods against k (number of topics).
```{r, eval=FALSE}
(k <- optimal_k(doc_term_mat, 40, control = control))
```
```{r, echo=FALSE}
(k <- optimal_k(doc_term_mat, 40, control = control, drop.seed = FALSE))
```
It appears the optimal number of topics is ~k = `r as.numeric(k)`.
### Run the Model
```{r}
control[["seed"]] <- 100
lda_model <- topicmodels::LDA(doc_term_mat, k=as.numeric(k), method = "Gibbs",
control = control)
```
### Plot the Topics Per Person & Time
```{r, fig.width=10, fig.height=12}
topics <- topicmodels::posterior(lda_model, doc_term_mat)[["topics"]]
topic_dat <- dplyr::add_rownames(as.data.frame(topics), "Person_Time")
colnames(topic_dat)[-1] <- apply(terms(lda_model, 10), 2, paste, collapse = ", ")
tidyr::gather(topic_dat, Topic, Proportion, -c(Person_Time)) %>%
tidyr::separate(Person_Time, c("Person", "Time"), sep = "_") %>%
dplyr::mutate(Person = factor(Person,
levels = c("OBAMA", "ROMNEY", "LEHRER", "SCHIEFFER", "CROWLEY", "QUESTION" ))
) %>%
ggplot2::ggplot(ggplot2::aes(weight=Proportion, x=Topic, fill=Topic)) +
ggplot2::geom_bar() +
ggplot2::coord_flip() +
ggplot2::facet_grid(Person~Time) +
ggplot2::guides(fill=FALSE) +
ggplot2::xlab("Proportion")
```
### Plot the Topics Matrix as a Heatmap
```{r}
heatmap(topics, scale = "none")
```
### Network of the Word Distributions Over Topics (Topic Relation)
```{r}
post <- topicmodels::posterior(lda_model)
cor_mat <- cor(t(post[["terms"]]))
cor_mat[ cor_mat < .05 ] <- 0
diag(cor_mat) <- 0
graph <- graph.adjacency(cor_mat, weighted=TRUE, mode="lower")
graph <- delete.edges(graph, E(graph)[ weight < 0.05])
E(graph)$edge.width <- E(graph)$weight*20
V(graph)$label <- paste("Topic", V(graph))
V(graph)$size <- colSums(post[["topics"]]) * 15
par(mar=c(0, 0, 3, 0))
set.seed(110)
plot.igraph(graph, edge.width = E(graph)$edge.width,
edge.color = "orange", vertex.color = "orange",
vertex.frame.color = NA, vertex.label.color = "grey30")
title("Strength Between Topics Based On Word Probabilities", cex.main=.8)
```
### Network of the Topics Over Dcouments (Topic Relation)
```{r, fig.width=8, fig.height=8}
minval <- .1
topic_mat <- topicmodels::posterior(lda_model)[["topics"]]
graph <- graph_from_incidence_matrix(topic_mat, weighted=TRUE)
graph <- delete.edges(graph, E(graph)[ weight < minval])
E(graph)$edge.width <- E(graph)$weight*17
E(graph)$color <- "blue"
V(graph)$color <- ifelse(grepl("^\\d+$", V(graph)$name), "grey75", "orange")
V(graph)$frame.color <- NA
V(graph)$label <- ifelse(grepl("^\\d+$", V(graph)$name), paste("topic", V(graph)$name), gsub("_", "\n", V(graph)$name))
V(graph)$size <- c(rep(10, nrow(topic_mat)), colSums(topic_mat) * 20)
V(graph)$label.color <- ifelse(grepl("^\\d+$", V(graph)$name), "red", "grey30")
par(mar=c(0, 0, 3, 0))
set.seed(369)
plot.igraph(graph, edge.width = E(graph)$edge.width,
vertex.color = adjustcolor(V(graph)$color, alpha.f = .4))
title("Topic & Document Relationships", cex.main=.8)
```
### LDAvis of Model
The output from **LDAvis** is not easily embedded within an R markdown document, however, the reader may [see the results here](http://trinker.github.io/LDAvis/example/).
```{r, eval=FALSE}
lda_model %>%
topicmodels2LDAvis() %>%
LDAvis::serVis()
```
```{r, echo=FALSE, message=FALSE, results="hide"}
targ <- "C:/Users/Tyler/GitHub/trinker.github.com/LDAvis/example/lda.json"
unlink(targ,,TRUE)
temp <- tempfile()
lda_model %>%
topicmodels2LDAvis() %>%
LDAvis::serVis(temp, open.browser = FALSE) %>%
invisible()
file.copy(file.path(temp, "lda.json"), pathr::parse_path(targ) %>% pathr::front())
pathr::open_path("C:/Users/Tyler/GitHub/trinker.github.com/trinker.github.com.Rproj")
```
### Apply Model to New Data
```{r, eval=FALSE}
## Create the DocumentTermMatrix for New Data
doc_term_mat2 <- partial_republican_debates_2015 %>%
with(gofastr::q_dtm_stem(dialogue, paste(person, location, sep = "_"))) %>%
gofastr::remove_stopwords(stops, stem=TRUE) %>%
gofastr::filter_tf_idf() %>%
gofastr::filter_documents()
## Update Control List
control2 <- control
control2[["estimate.beta"]] <- FALSE
## Run the Model for New Data
lda_model2 <- topicmodels::LDA(doc_term_mat2, k = k, model = lda_model,
control = list(seed = 100, estimate.beta = FALSE))
## Plot the Topics Per Person & Location for New Data
topics2 <- topicmodels::posterior(lda_model2, doc_term_mat2)[["topics"]]
topic_dat2 <- dplyr::add_rownames(as.data.frame(topics2), "Person_Location")
colnames(topic_dat2)[-1] <- apply(terms(lda_model2, 10), 2, paste, collapse = ", ")
tidyr::gather(topic_dat2, Topic, Proportion, -c(Person_Location)) %>%
tidyr::separate(Person_Location, c("Person", "Location"), sep = "_") %>%
ggplot2::ggplot(ggplot2::aes(weight=Proportion, x=Topic, fill=Topic)) +
ggplot2::geom_bar() +
ggplot2::coord_flip() +
ggplot2::facet_grid(Person~Location) +
ggplot2::guides(fill=FALSE) +
ggplot2::xlab("Proportion")
## LDAvis of Model for New Data
lda_model2 %>%
topicmodels2LDAvis() %>%
LDAvis::serVis()
```
# Contributing
You are welcome to:
* submit suggestions and bug-reports at: <https://github.com/trinker/topicmodels_learning/issues>
* send a pull request on: <https://github.com/trinker/topicmodels_learning/>
* compose a friendly e-mail to: <[email protected]>