From cd80836982afb02203459db7bb481f71d406fd5f Mon Sep 17 00:00:00 2001
From: Michael Clark
-
-
Donald Barthelme
“You may not be interested in absurdity, but absurdity is interested in you.”
This short story is essentially a how-to on parenting.
@@ -364,22 +387,22 @@We noted in the Shakespeare start to finish example that there are faster alternatives than the standard LDA in topicmodels. In particular, the powerful text2vec package contains a faster and less memory intensive implementation of LDA and dealing with text generally. Both of which are very important if you’re wanting to use R for text analysis. The other nice thing is that it works with LDAvis for visualization.
For the following, we’ll use one of the partially cleaned document term matrix for the Shakespeare texts. One of the things to get used to is that text2vec uses the newer R6 classes of R objects, hence the $
approach you see to using specific methods.
library(text2vec)
-load('data/shakes_dtm_stemmed.RData')
-# load('data/shakes_words_df.RData') # non-stemmed
-
-# convert to the sparse matrix representation using Matrix package
-shakes_dtm = as(shakes_dtm, 'CsparseMatrix')
-
-# setup the model
-lda_model = LDA$new(n_topics = 10, doc_topic_prior = 0.1, topic_word_prior = 0.01)
-
-# fit the model
-doc_topic_distr = lda_model$fit_transform(x = shakes_dtm,
- n_iter = 1000,
- convergence_tol = 0.0001,
- n_check_convergence = 25,
- progressbar = FALSE)
library(text2vec)
+load('data/shakes_dtm_stemmed.RData')
+# load('data/shakes_words_df.RData') # non-stemmed
+
+# convert to the sparse matrix representation using Matrix package
+shakes_dtm = as(shakes_dtm, 'CsparseMatrix')
+
+# setup the model
+lda_model = LDA$new(n_topics = 10, doc_topic_prior = 0.1, topic_word_prior = 0.01)
+
+# fit the model
+doc_topic_distr = lda_model$fit_transform(x = shakes_dtm,
+ n_iter = 1000,
+ convergence_tol = 0.0001,
+ n_check_convergence = 25,
+ progressbar = FALSE)
INFO [2018-03-06 19:16:15] iter 25 loglikelihood = -1746173.024
INFO [2018-03-06 19:16:16] iter 50 loglikelihood = -1683541.903
INFO [2018-03-06 19:16:17] iter 75 loglikelihood = -1660985.396
@@ -390,7 +413,7 @@ A Faster LDA
INFO [2018-03-06 19:16:20] iter 200 loglikelihood = -1636356.883
INFO [2018-03-06 19:16:21] iter 225 loglikelihood = -1636487.222
INFO [2018-03-06 19:16:21] early stopping at 225 iteration
-
+
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "prai" "hear" "ey" "love" "word" "natur" "night" "god" "friend" "death"
[2,] "honor" "madam" "sweet" "dai" "letter" "fortun" "fear" "dai" "hand" "grace"
@@ -402,11 +425,11 @@ A Faster LDA
[8,] "hear" "lose" "black" "heart" "reason" "truth" "bed" "fight" "leav" "hope"
[9,] "heart" "strang" "kiss" "marri" "hand" "leav" "mad" "sword" "deed" "heaven"
[10,] "friend" "sister" "sun" "night" "talk" "command" "hand" "heart" "tear" "die"
-
+
[1] 7
-# top-words could be sorted by “relevance” which also takes into account
-# frequency of word in the corpus (0 < lambda < 1)
-lda_model$get_top_words(n = 10, topic_number = 1:10, lambda = 0.2)
# top-words could be sorted by “relevance” which also takes into account
+# frequency of word in the corpus (0 < lambda < 1)
+lda_model$get_top_words(n = 10, topic_number = 1:10, lambda = 0.2)
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
[1,] "honest" "madam" "ey" "love" "letter" "natur" "ear" "england" "rome" "bloodi"
[2,] "beseech" "sea" "cheek" "youth" "merri" "report" "sleep" "majesti" "deed" "royal"
@@ -418,8 +441,8 @@ A Faster LDA
[8,] "matter" "entreat" "mortal" "song" "horn" "virgin" "poison" "battl" "kneel" "flourish"
[9,] "fellow" "seek" "wing" "paint" "bond" "wine" "shake" "harri" "fly" "king"
[10,] "walk" "passion" "short" "wed" "troth" "direct" "move" "crown" "wert" "tide"
-
+
Given that most text analysis can be very time consuming for a model, consider any approach that might give you more efficiency.
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