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PORemoving an "empty" character item from a corpus of documents in R?
 

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  1. PORemoving an "empty" character item from a corpus of documents in R?
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    <p>I am using the <code>tm</code> and <code>lda</code> packages in R to topic model a corpus of news articles. However, I am getting a "non-character" problem represented as <code>""</code> that is messing up my topics. Here is my workflow:</p> <pre><code>text &lt;- Corpus(VectorSource(d$text)) newtext &lt;- lapply(text, tolower) sw &lt;- c(stopwords("english"), "ahram", "online", "egypt", "egypts", "egyptian") newtext &lt;- lapply(newtext, function(x) removePunctuation(x)) newtext &lt;- lapply(newtext, function(x) removeWords(x, sw)) newtext &lt;- lapply(newtext, function(x) removeNumbers(x)) newtext &lt;- lapply(newtext, function(x) stripWhitespace(x)) d$processed &lt;- unlist(newtext) corpus &lt;- lexicalize(d$processed) k &lt;- 40 result &lt;-lda.collapsed.gibbs.sampler(corpus$documents, k, corpus$vocab, 500, .02, .05, compute.log.likelihood = TRUE, trace = 2L) </code></pre> <p>Unfortunately, when I train the lda model, everything looks great except the most frequently occurring word is "". I try to remedy this by removing it from the vocab as given below and reestimating the model just as above: </p> <pre><code>newtext &lt;- lapply(newtext, function(x) removeWords(x, "")) </code></pre> <p>But, it's still there, as evidenced by:</p> <pre><code>str_split(newtext[[1]], " ") [[1]] [1] "" "body" "mohamed" "hassan" [5] "cook" "found" "turkish" "search" [9] "rescue" "teams" "rescued" "hospital" [13] "rescue" "teams" "continued" "search" [17] "missing" "body" "cook" "crew" [21] "wereegyptians" "sudanese" "syrians" "hassan" [25] "cook" "cargo" "ship" "sea" [29] "bright" "crashed" "thursday" "port" [33] "antalya" "southern" "turkey" "vessel" [37] "collided" "rocks" "port" "thursday" [41] "night" "result" "heavy" "winds" [45] "waves" "crew" "" </code></pre> <p>Any suggestions on how to go about removing this? Adding <code>""</code> to my list of stopwords doesn't help, either.</p>
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    1. COHave you tried `stripWhitespace` with `tm_map`, and doing it first, before the other transformations?
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    2. CO@Ben Yes -- for reasons unimportant here, I don't want to convert to a dtm, which is required for `tm_map`.
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    3. COrighto, no worries. Have you had any success using `lda` for Author-Topic models, or Topics Over Time models?
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