R: How to Prepare Data for LDA/Text Analysis - r

Question

I am working with the R programming language.
I would like to perform BTM (Bitopic Term Model - a variant of LDA (Latent Dirichlet Analysis) for small text datasets) on some text data. I am following this tutorial over here: https://cran.r-project.org/web/packages/BTM/readme/README.html
When I look at the dataset ("brussels_reviews_anno") being used in this tutorial, it look something like this (I can not recognize the format of this data!):
library(udpipe)
library(BTM)
data("brussels_reviews_anno", package = "udpipe")
head(brussels_reviews_anno)
doc_id language sentence_id token_id token lemma upos xpos
1 32198807 es 1 1 Gwen gwen NOUN NNP
2 32198807 es 1 2 fue ser VERB VB
3 32198807 es 1 3 una un DET DT
4 32198807 es 1 4 magnifica magnifica NOUN NN
5 32198807 es 1 5 anfitriona anfitriono ADJ JJ
6 32198807 es 1 6 . . PUNCT .
My dataset ("my_data") is in the current format - I manually create a text dataset for this example using reviews of fast food restaurants found on the internet:
my_data = structure(list(id = 1:8, reviews = c("I guess the employee decided to buy their lunch with my card my card hoping I wouldn't notice but since it took so long to run my car I want to head and check my bank account and sure enough they had bought food on my card that I did not receive leave. Had to demand for and for a refund because they acted like it was my fault and told me the charges are still pending even though they are for 2 different amounts.",
"I went to McDonald's and they charge me 50 for Big Mac when I only came with 49. The casher told me that I can't read correctly and told me to get glasses. I am file a report on your casher and now I'm mad.",
"I really think that if you can buy breakfast anytime then I should be able to get a cheeseburger anytime especially since I really don't care for breakfast food. I really like McDonald's food but I preferred tree lunch rather than breakfast. Thank you thank you thank you.",
"I guess the employee decided to buy their lunch with my card my card hoping I wouldn't notice but since it took so long to run my car I want to head and check my bank account and sure enough they had bought food on my card that I did not receive leave. Had to demand for and for a refund because they acted like it was my fault and told me the charges are still pending even though they are for 2 different amounts.",
"Never order McDonald's from Uber or Skip or any delivery service for that matter, most particularly one on Elgin Street and Rideau Street, they never get the order right. Workers at either of these locations don't know how to follow simple instructions. Don't waste your money at these two locations.",
"Employees left me out in the snow and wouldn’t answer the drive through. They locked the doors and it was freezing. I asked the employee a simple question and they were so stupid they answered a completely different question. Dumb employees and bad food.",
"McDonalds food was always so good but ever since they add new/more crispy chicken sandwiches it has come out bad. At first I thought oh they must haven't had a good day but every time I go there now it's always soggy, and has no flavor. They need to fix this!!!",
"I just ordered the new crispy chicken sandwich and I'm very disappointed. Not only did it taste horrible, but it was more bun than chicken. Not at all like the commercial shows. I hate sweet pickles and there were two slices on my sandwich. I wish I could add a photo to show the huge bun and tiny chicken."
)), class = "data.frame", row.names = c(NA, -8L))
Can someone please show me how I can take my dataset and transform it in such a way that I can perform BTM analysis on this data and create a visualization similar to the visualizations in this tutorial?
Thanks!
Additional References:
https://rforanalytics.com/11-7-topic-modelling.html

Answer 1

The class of brussels_reviews_anno is just a regular data.frame. That structure is generated by the function udpipe() from the package udpipe.
Below I provide a working example, with the exclusion of the path where I save the language model, that shows how to replicate a similar data structure.
Please keep in mind that udpipe() does a lot of stuff. The reason why you see many more columns in the final data.frame out is because I did not tweak any parameters of the function nor simply deleted any of the columns.
Overall, to get started with BTM() you need to tokenize your textual data. That's one of the things you can do with the package udpipe.
Hope this helped!
library(udpipe)
library(BTM)
data("brussels_reviews_anno", package = "udpipe")
head(brussels_reviews_anno)
#> doc_id language sentence_id token_id token lemma upos xpos
#> 1 32198807 es 1 1 Gwen gwen NOUN NNP
#> 2 32198807 es 1 2 fue ser VERB VB
#> 3 32198807 es 1 3 una un DET DT
#> 4 32198807 es 1 4 magnifica magnifica NOUN NN
#> 5 32198807 es 1 5 anfitriona anfitriono ADJ JJ
#> 6 32198807 es 1 6 . . PUNCT .
my_data = structure(list(id = 1:8, reviews = c("I guess the employee decided to buy their lunch with my card my card hoping I wouldn't notice but since it took so long to run my car I want to head and check my bank account and sure enough they had bought food on my card that I did not receive leave. Had to demand for and for a refund because they acted like it was my fault and told me the charges are still pending even though they are for 2 different amounts.",
"I went to McDonald's and they charge me 50 for Big Mac when I only came with 49. The casher told me that I can't read correctly and told me to get glasses. I am file a report on your casher and now I'm mad.",
"I really think that if you can buy breakfast anytime then I should be able to get a cheeseburger anytime especially since I really don't care for breakfast food. I really like McDonald's food but I preferred tree lunch rather than breakfast. Thank you thank you thank you.",
"I guess the employee decided to buy their lunch with my card my card hoping I wouldn't notice but since it took so long to run my car I want to head and check my bank account and sure enough they had bought food on my card that I did not receive leave. Had to demand for and for a refund because they acted like it was my fault and told me the charges are still pending even though they are for 2 different amounts.",
"Never order McDonald's from Uber or Skip or any delivery service for that matter, most particularly one on Elgin Street and Rideau Street, they never get the order right. Workers at either of these locations don't know how to follow simple instructions. Don't waste your money at these two locations.",
"Employees left me out in the snow and wouldn’t answer the drive through. They locked the doors and it was freezing. I asked the employee a simple question and they were so stupid they answered a completely different question. Dumb employees and bad food.",
"McDonalds food was always so good but ever since they add new/more crispy chicken sandwiches it has come out bad. At first I thought oh they must haven't had a good day but every time I go there now it's always soggy, and has no flavor. They need to fix this!!!",
"I just ordered the new crispy chicken sandwich and I'm very disappointed. Not only did it taste horrible, but it was more bun than chicken. Not at all like the commercial shows. I hate sweet pickles and there were two slices on my sandwich. I wish I could add a photo to show the huge bun and tiny chicken."
)), class = "data.frame", row.names = c(NA, -8L))
# download a language model
udpipe_download_model("english-ewt", model_dir = "~/Desktop/")
#> Downloading udpipe model from https://raw.githubusercontent.com/jwijffels/udpipe.models.ud.2.5/master/inst/udpipe-ud-2.5-191206/english-ewt-ud-2.5-191206.udpipe to ~/Desktop//english-ewt-ud-2.5-191206.udpipe
#> - This model has been trained on version 2.5 of data from https://universaldependencies.org
#> - The model is distributed under the CC-BY-SA-NC license: https://creativecommons.org/licenses/by-nc-sa/4.0
#> - Visit https://github.com/jwijffels/udpipe.models.ud.2.5 for model license details.
#> - For a list of all models and their licenses (most models you can download with this package have either a CC-BY-SA or a CC-BY-SA-NC license) read the documentation at ?udpipe_download_model. For building your own models: visit the documentation by typing vignette('udpipe-train', package = 'udpipe')
#> Downloading finished, model stored at '~/Desktop//english-ewt-ud-2.5-191206.udpipe'
#> language file_model
#> 1 english-ewt ~/Desktop//english-ewt-ud-2.5-191206.udpipe
#> url
#> 1 https://raw.githubusercontent.com/jwijffels/udpipe.models.ud.2.5/master/inst/udpipe-ud-2.5-191206/english-ewt-ud-2.5-191206.udpipe
#> download_failed download_message
#> 1 FALSE OK
# load in the environment
eng_model = udpipe_load_model("~/Desktop/english-ewt-ud-2.5-191206.udpipe")
# apply the tokenization
out = udpipe(my_data$reviews, object = eng_model)
head(out)
#> doc_id paragraph_id sentence_id
#> 1 doc1 1 1
#> 2 doc1 1 1
#> 3 doc1 1 1
#> 4 doc1 1 1
#> 5 doc1 1 1
#> 6 doc1 1 1
#> sentence
#> 1 I guess the employee decided to buy their lunch with my card my card hoping I wouldn't notice but since it took so long to run my car I want to head and check my bank account and sure enough they had bought food on my card that I did not receive leave.
#> 2 I guess the employee decided to buy their lunch with my card my card hoping I wouldn't notice but since it took so long to run my car I want to head and check my bank account and sure enough they had bought food on my card that I did not receive leave.
#> 3 I guess the employee decided to buy their lunch with my card my card hoping I wouldn't notice but since it took so long to run my car I want to head and check my bank account and sure enough they had bought food on my card that I did not receive leave.
#> 4 I guess the employee decided to buy their lunch with my card my card hoping I wouldn't notice but since it took so long to run my car I want to head and check my bank account and sure enough they had bought food on my card that I did not receive leave.
#> 5 I guess the employee decided to buy their lunch with my card my card hoping I wouldn't notice but since it took so long to run my car I want to head and check my bank account and sure enough they had bought food on my card that I did not receive leave.
#> 6 I guess the employee decided to buy their lunch with my card my card hoping I wouldn't notice but since it took so long to run my car I want to head and check my bank account and sure enough they had bought food on my card that I did not receive leave.
#> start end term_id token_id token lemma upos xpos
#> 1 1 1 1 1 I I PRON PRP
#> 2 3 7 2 2 guess guess VERB VBP
#> 3 9 11 3 3 the the DET DT
#> 4 13 20 4 4 employee employee NOUN NN
#> 5 22 28 5 5 decided decide VERB VBD
#> 6 30 31 6 6 to to PART TO
#> feats head_token_id dep_rel deps misc
#> 1 Case=Nom|Number=Sing|Person=1|PronType=Prs 2 nsubj <NA> <NA>
#> 2 Mood=Ind|Tense=Pres|VerbForm=Fin 0 root <NA> <NA>
#> 3 Definite=Def|PronType=Art 4 det <NA> <NA>
#> 4 Number=Sing 5 nsubj <NA> <NA>
#> 5 Mood=Ind|Tense=Past|VerbForm=Fin 2 ccomp <NA> <NA>
#> 6 <NA> 7 mark <NA> <NA>
Created on 2022-09-20 by the reprex package (v2.0.1)