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They say that a picture is worth a thousand words. With the WordCloud Generator your pictures will be converted into individual, typographical images, which can be used as a personal gift or as the base for further graphics.
In this article, I will show you how to use text data to build word clouds in R. We will use a dataset containing around 200k Jeopardy questions. The dataset can be downloaded here (thanks to reddit user trexmatt for providing the dataset). We will require three packages for this: tm, SnowballC, and wordcloud. First, […]
Cloudy with a Chance of Words Lots of fun projects in the works, so today's post will be short - a demonstration on how to create wordclouds, both with and without sentiment analysis results. While I could use song lyrics again, I decided to use a different dataset that comes with the quanteda packages: all 58 Inaugural Addresses, from Washington's first speech in 1789 to Trump's in 2017.library(quanteda) #install with install.packages("quanteda") if neededdata(data_corpus_inaugural)speeches %count(word, sort = TRUE)obama_words %>%inner_join(get_sentiments("nrc") %>%filter(sentiment %in% c("positive","negative"))) %>%filter(n > 1) %>%acast(word ~ sentiment, value.var = "n", fill = 0) %>%comparison.cloud(colors = c("red","blue"))## Joining, by = "word"The acast statement reshapes the data, putting our sentiments of positive and negative as separate columns. Setting fill = 0 is important, since a negative word will be missing a value for the positive column and vice versa; without fill = 0, it would drop any row with NA in one of the columns (which would be every word in the set). As a sidenote, we could use the comparison cloud to compare words across two documents, such as comparing two Presidents. The columns would be counts for each President, as opposed to count by sentiment.Interestingly, the NRC classifies "government" and "words" as negative. But even if we ignore those two words, which are Obama's most frequent, the negatively-valenced words are much larger than most of his positively-valenced words. So while he uses many more positively-valenced words than negatively-valenced words - seen by the sheer number of blue words - he uses the negatively-valenced words more often. If you were so inclined, you could probably run a sentiment analysis on his speeches and see if they tend to be more positive or negative, and/or if they follow arcs of negativity and positivity. And feel free to generate your own wordcloud: all you'd need to do is change the filter(President == "") to whatever President you're interested in examining (or whatever text data you'd like to use, if President's speeches aren't your thing).
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Connie. Show and wear this popular beautiful female first name designed as colorful wordcloud made of horizontal and vertical cursive hand lettering typography in dusty pale vivid hot barbie pink colors.
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Amélia, a timeless and elegant name, has origins dating back to ancient Greece, meaning "industrious" and "striving for excellence." It embodies the strength and determination of women who have paved the way for progress throughout history. So, embrace your inner Amélia and strive to achieve greatness in all that you do. Let your name be a reminder of your resilience and ability to overcome any obstacle with grace and determination.
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