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Analysis of moods in R

I am new to analytic analysis and have absolutely no idea how to do this using R. Therefore, I would like help and guidance on this.

I have an opinion dataset and would like to analyze opinions.

Title Date Content Boy May 13 2015 "She is pretty", Tom said. Animal June 14 2015 The penguin is cute, lion added. Human March 09 2015 Mr Koh predicted that every human is smart.. Monster Jan 22 2015 Ms May, a student, said that John has $10.80.

Thanks.

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Analysis of moods includes a wide category of methods designed to measure positive and negative moods from the text, so this is a rather difficult question to answer simply. But here is a simple answer: you can apply the dictionary to your matrix of documents and then combine the positive and negative key categories of your dictionary to create a measure of mood.

I suggest trying this in the quanteda text analysis package, which processes many existing dictionary formats and allows you to create very flexible user dictionaries.

For instance:

 require(quanteda) mycorpus <- subset(inaugCorpus, Year>1980) mydict <- dictionary(list(negative = c("detriment*", "bad*", "awful*", "terrib*", "horribl*"), postive = c("good", "great", "super*", "excellent"))) myDfm <- dfm(mycorpus, dictionary = mydict) ## Creating a dfm from a corpus ... ## ... lowercasing ## ... tokenizing ## ... indexing documents: 9 documents ## ... indexing features: 3,113 feature types ## ... applying a dictionary consisting of 2 keys ## ... created a 9 x 2 sparse dfm ## ... complete. ## Elapsed time: 0.057 seconds. myDfm ## Document-feature matrix of: 9 documents, 2 features. ## 9 x 2 sparse Matrix of class "dfmSparse" ## features ## docs negative postive ## 1981-Reagan 0 6 ## 1985-Reagan 0 6 ## 1989-Bush 0 18 ## 1993-Clinton 1 2 ## 1997-Clinton 2 8 ## 2001-Bush 1 6 ## 2005-Bush 0 8 ## 2009-Obama 2 3 ## 2013-Obama 1 3 # use a LIWC dictionary - obviously you need this file liwcdict <- dictionary(file = "LIWC2001_English.dic", format = "LIWC") myDfmLIWC <- dfm(mycorpus, dictionary = liwcdict) ## Creating a dfm from a corpus ... ## ... lowercasing ## ... tokenizing ## ... indexing documents: 9 documents ## ... indexing features: 3,113 feature types ## ... applying a dictionary consisting of 68 keys ## ... created a 9 x 68 sparse dfm ## ... complete. ## Elapsed time: 1.844 seconds. myDfmLIWC[, grep("^Pos|^Neg", features(myDfmLIWC))] ## Document-feature matrix of: 9 documents, 4 features. ## 9 x 4 sparse Matrix of class "dfmSparse" ## features ## docs Negate Posemo Posfeel Negemo ## 1981-Reagan 46 89 5 24 ## 1985-Reagan 28 104 7 33 ## 1989-Bush 40 102 10 8 ## 1993-Clinton 25 51 3 23 ## 1997-Clinton 27 64 5 22 ## 2001-Bush 40 80 6 27 ## 2005-Bush 25 117 5 31 ## 2009-Obama 40 83 5 46 ## 2013-Obama 42 80 13 22

For your enclosure, assuming you are in a data.frame file called data , you can create a quanteda enclosure using:

 mycorpus <- corpus(data$Content, docvars = data[, 1:2])

See also ?textfile for loading contents from files in one simple command. This works, for example, with .csv files, although you will have problems with this file because the "Content" field contains text containing commas.

There are many other ways to measure feelings, of course, but if you are new to Mining and R, this should get you started. You can learn more about mining methods (and apologies if you have already encountered them):

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