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<h1>LDA Topic Modeling</h1>
<p><em>© 2014 Wouter van Atteveldt, license: [CC-BY-SA]</em></p>
<p>Latent Dirichlet Allocation is a topic modeling algorithm that automatically clusters words that for a cohesive pattern of co-occurrence.
LDA assumes a 'generative model', where a text is generated by selecting one or more topics, and then drawing words from each of those topics.
Thus, each document has multiple topics and each word can occur in multiple topics. </p>
<h2>Creating a topic model</h2>
<p>Topic models are constructed directly from a term-document matrix using the <code>topicmodels</code> package.
As before, we use the <code>create_matrix</code> function from the <code>RTextTools</code> package to create the term-document matrix from a set of customer reviews.
Note that we need to remove empty rows or columns (e.g. empty reviews).
The <code>achmea.csv</code> file can be downloaded from <a href="https://raw.githubusercontent.com/vanatteveldt/learningr/master/achmea.csv">github</a>.</p>
<pre><code class="r">library(RTextTools)
</code></pre>
<pre><code>## Loading required package: SparseM
##
## Attaching package: 'SparseM'
##
## The following object is masked from 'package:base':
##
## backsolve
</code></pre>
<pre><code class="r">library(slam)
d = read.csv("achmea.csv")
m = create_matrix(d$CONTENT, language="dutch", removeStopwords=T, )
m = m[row_sums(m) > 0,col_sums(m) > 0]
dim(m)
</code></pre>
<pre><code>## [1] 21229 29022
</code></pre>
<p>Now, we can fit the topic model, say with k=10 topics and alpha=.5.
(A smaller alpha means that topics are more 'concentrated' in the documents)</p>
<pre><code class="r">library(topicmodels)
fit = LDA(m, k=10, method="Gibbs", control=list(iter=500, alpha=.5))
</code></pre>
<p>We can visually inspect the words per topics using the <code>terms</code> function:</p>
<pre><code class="r">terms(fit, 10)
</code></pre>
<pre><code>## Topic 1 Topic 2 Topic 3 Topic 4
## [1,] "zorgverzekering" "centraalbeheer" "0900" "httpt"
## [2,] "fbto" "fbtowebcare" "fbto" "centraalbeheer"
## [3,] "2014" "evenapeldoornbellen" "mensen" "autoverzekering"
## [4,] "echt" "jullie" "ver" "gratis"
## [5,] "ditzo" "httpt" "echt" "afsluiten"
## [6,] "premies" "fbto" "gaan" "keuze"
## [7,] "zorgpremie" "weer" "weten" "via"
## [8,] "vergelijken" "bedankt" "helpen" "vraagt"
## [9,] "jaar" "lekker" "vinden" "scherpe"
## [10,] "zorgve" "nieuwe" "paar" "autocheck"
## Topic 5 Topic 6 Topic 7 Topic 8
## [1,] "fbto" "centraal" "apeldoorn" "beheer"
## [2,] "wel" "beheer" "even" "centraal"
## [3,] "verzekering" "achmea" "bellen" "the"
## [4,] "jullie" "httpt" "httpt" "commercial"
## [5,] "jaar" "2014" "volkert" "car"
## [6,] "fbtowebcare" "midwintermarathon" "nieuwe" "and"
## [7,] "eigen" "week" "reclame" "achmea"
## [8,] "premie" "uitslag" "winter" "banned"
## [9,] "verzekerd" "schreef" "echte" "driving"
## [10,] "per" "online" "youtube" "self"
## Topic 9 Topic 10
## [1,] "httpt" "goed"
## [2,] "auto" "snelle"
## [3,] "fbto" "goede"
## [4,] "nieuwe" "snel"
## [5,] "zelfrijdende" "afhandeling"
## [6,] "beheer" "schade"
## [7,] "centraal" "via"
## [8,] "commercial" "zeer"
## [9,] "via" "verzekeringen"
## [10,] "achmea" "service"
</code></pre>
<p>And let's make a word cloud of the first topic:</p>
<pre><code class="r">library(RColorBrewer)
library(wordcloud)
x = posterior(fit)$terms[1,]
x = sort(x, decreasing=T)[1:100]
x = x[!is.na(x)]
pal <- brewer.pal(6,"YlGnBu")
wordcloud(names(x), x, scale=c(6,.5), min.freq=1, max.words=Inf, random.order=FALSE, rot.per=.15, colors=pal)
</code></pre>
<pre><code>## Warning: zorgverzekering could not be fit on page. It will not be plotted.
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-4"/> </p>
<p>As in the 'Corpus Analysis' howto, we can define a function to compute the term statistics to filter on informative words:</p>
<pre><code class="r">library(tm)
</code></pre>
<pre><code>## Loading required package: NLP
</code></pre>
<pre><code class="r">term.statistics <- function(dtm) {
dtm = dtm[row_sums(dtm) > 0,col_sums(dtm) > 0] # get rid of empty rows/columns
vocabulary = colnames(dtm)
data.frame(term = vocabulary,
characters = nchar(vocabulary),
number = grepl("[0-9]", vocabulary),
nonalpha = grepl("\\W", vocabulary),
termfreq = col_sums(dtm),
docfreq = col_sums(dtm > 0),
reldocfreq = col_sums(dtm > 0) / nDocs(dtm),
tfidf = tapply(dtm$v/row_sums(dtm)[dtm$i], dtm$j, mean) * log2(nDocs(dtm)/col_sums(dtm > 0)))
}
terms = term.statistics(m)
words = terms$term[order(-terms$tfidf)[1:10000]]
m_filtered = m[, colnames(m) %in% words]
m_filtered = m_filtered[row_sums(m_filtered) > 0,col_sums(m_filtered) > 0]
fit = LDA(m_filtered, k=10, method="Gibbs", control=list(iter=500, alpha=.5))
terms(fit, 10)
</code></pre>
<pre><code>## Topic 1 Topic 2 Topic 3 Topic 4
## [1,] "nee" "snelheid" "rtalsjehetziet" "peterrdev"
## [2,] "utrecht" "afwikkeling" "caravan" "cos6ypex2e7s"
## [3,] "ziyech" "bereikbaarheid" "uitzetten" "markhoekx"
## [4,] "nemo" "duidelijkheid" "tarieven" "bedenken"
## [5,] "goedemorgen" "vlotte" "verwerking" "hypotheken"
## [6,] "thanks" "afhandelen" "aanuit" "eindhoven"
## [7,] "briljantreclame" "correcte" "hond" "flexibel"
## [8,] "nvt" "eenvoudig" "bellenäò" "redelijke"
## [9,] "commercieel" "vriendelijkheid" "betalingen" "makkelijker"
## [10,] "sotsji" "nakomen" "maat" "nee"
## Topic 5 Topic 6 Topic 7
## [1,] "icoins" "prima" "peterrdev"
## [2,] "punten" "redelijk" "jorid"
## [3,] "klantvriendelijkste" "muts" "uitbetaling"
## [4,] "beoordeling" "helder" "gezeur"
## [5,] "moment" "desk" "fijne"
## [6,] "negatieve" "kut" "guuskuijer"
## [7,] "vlot" "beschikbaar" "verzekeringsmap"
## [8,] "cocnrn3f1sap" "eerlijke" "overzichtelijke"
## [9,] "verbeterpunten" "accountmanager" "woont"
## [10,] "verstuurd" "justronaldd" "cba"
## Topic 8 Topic 9 Topic 10
## [1,] "uitzetten" "moment" "prijs"
## [2,] "afspraak" "correcte" "klantvriendelijk"
## [3,] "duur" "korte" "kwaliteit"
## [4,] "prima" "sorry" "verhouding"
## [5,] "attent" "nette" "behulpzaam"
## [6,] "werknemer" "onlinemarketeer" "bereikbaarheid"
## [7,] "koersen" "terugkoppeling" "betrouwbaar"
## [8,] "correcte" "harte" "gemakkelijk"
## [9,] "noemen" "verzonden" "heldere"
## [10,] "flexibiliteit" "virupa" "carolien"
</code></pre>
<h2>Creating a topic model per sentiment category</h2>
<p>We can also make a topic model of a subset of the data, for example of all the negative reviews:</p>
<pre><code class="r">neg = d$CONTENT[!is.na(d$SENTIMENT) & d$SENTIMENT == -1]
m_neg = create_matrix(neg, removeStopwords=T, language="dutch")
m_neg = m_neg[row_sums(m_neg) > 0,col_sums(m_neg) > 0]
fit = LDA(m_neg, k=10, method="Gibbs", control=list(iter=500, alpha=.5))
terms(fit, 10)
</code></pre>
<pre><code>## Topic 1 Topic 2 Topic 3 Topic 4 Topic 5
## [1,] "mail" "module" "eigen" "communicatie" "schade"
## [2,] "vraag" "maanden" "risico" "goed" "fbto"
## [3,] "antwoord" "lang" "betalen" "zeer" "keer"
## [4,] "email" "modules" "erg" "fbto" "wel"
## [5,] "telefonisch" "wachttijd" "bedrag" "slecht" "goed"
## [6,] "kreeg" "wel" "per" "slechte" "steeds"
## [7,] "contact" "weer" "wel" "afspraken" "vergoed"
## [8,] "ontvangen" "jaar" "euro" "telefonische" "zeker"
## [9,] "per" "per" "jaar" "bereikbaarheid" "afhandeling"
## [10,] "reactie" "vergoeding" "betaald" "personeel" "gebeld"
## Topic 6 Topic 7 Topic 8 Topic 9
## [1,] "alleen" "via" "fbto" "klant"
## [2,] "fbto" "website" "verzekering" "klanten"
## [3,] "graag" "wel" "wij" "verzekeringen"
## [4,] "duidelijk" "soms" "reisverzekering" "jullie"
## [5,] "klant" "beter" "jaar" "fbto"
## [6,] "zorgverzekering" "alle" "moeten" "nieuwe"
## [7,] "declaraties" "site" "wel" "jaar"
## [8,] "declaratie" "mensen" "auto" "korting"
## [9,] "kosten" "moeten" "gaan" "jaren"
## [10,] "overzicht" "internet" "onze" "jammer"
## Topic 10
## [1,] "premie"
## [2,] "prijs"
## [3,] "fbto"
## [4,] "verzekering"
## [5,] "erg"
## [6,] "duur"
## [7,] "verzekerd"
## [8,] "autoverzekering"
## [9,] "betere"
## [10,] "verzekeringen"
</code></pre>
<h2>Extracting the topics per document</h2>
<p>If you want to e.g. correlate topics with sentiment or add the topics as features to the machine learning, it is useful to extract which documents belong to which topic.
The <code>fit</code> object contains the needed information, which can be cast into a matrix:</p>
<pre><code class="r">library(reshape2)
assignments = data.frame(i=fit@wordassignments$i, j=fit@wordassignments$j, v=fit@wordassignments$v)
docsums = acast(assignments, i ~ v, value.var='j', fun.aggregate=length)
dim(docsums)
</code></pre>
<pre><code>## [1] 2228 10
</code></pre>
<pre><code class="r">head(docsums)
</code></pre>
<pre><code>## 1 2 3 4 5 6 7 8 9 10
## 1 0 0 0 0 0 0 3 1 0 2
## 2 0 0 0 0 0 0 0 0 0 1
## 3 0 0 1 0 13 0 0 4 0 8
## 4 1 0 0 0 0 0 4 2 8 0
## 5 24 0 0 6 9 2 0 0 0 0
## 6 0 0 1 1 1 13 7 3 0 0
</code></pre>
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