Scalable Text Mining for Large Escanning Datsets
Synopsis:
Harvested the original data of approximately 5500 e-scanned articles from IBM Watson to the E-scanning database (called Mongo), and saved in disk drive. Sandard text processing and cleaning (“the”, “and”, “ing”, “;”, “:” etc.,) simplified the ensuing three types text analyses using R text mining packages.
DATA PROCESSING
docs <- tm_map(docs, removePunctuation)
docs <- tm_map(docs, removeNumbers)
docs <- tm_map(docs, removeWords, stopwords("english"))
docs <- tm_map(docs, stripWhitespace)
docs <- tm_map(docs, stemDocument) #Removing common word endings (e.g., "ing", "es", "s")
dtm <- DocumentTermMatrix(docs) #create matrices that help the futher analysis
dtmss <- removeSparseTerms(dtm, 0.06) # This makes a matrix that is 6% empty space, maximum. ANALYSIS
1. Most Frequent Words
#org by freq
freq <- colSums(as.matrix(dtm))
ord <- order(freq)
freq[tail(ord, n=10)] ## gas compani million generat year power said percent energi
## 37 39 43 45 46 50 52 69 102
## coal
## 1822. Word Cloud
library("wordcloud")## Loading required package: RColorBrewer#100 most frequently used words.
set.seed(142)
wordcloud(names(freq), freq, max.words=20, colors=brewer.pal(25, "Dark2"))## Warning in brewer.pal(25, "Dark2"): n too large, allowed maximum for palette Dark2 is 8
## Returning the palette you asked for with that many colors
3. Clustering
Creating a cluster of n (here n=4) using Clara for Clustering Large Applications. Compared to other partitioning methods such as pam, Clara can deal with much larger datasets. Clara is fully described in chapter 3 of Kaufman and Rousseeuw (1990) ``Finding Groups in Data’’
library("cluster")
d <- dist(t(dtmss), method = "euclidian")
clara(as.matrix(d), 4, samples = 8, sampsize = 10)## Call: clara(x = as.matrix(d), k = 4, samples = 8, sampsize = 10)
## Medoids:
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## thomson thread three threequart thursday time timelin
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## tmsnrtrsuhzja tmsnrtrsuhzjancoal tmsnrtrsuhzoc tmsnrtrsuhzocnthe
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## told ton tonn tonnag tonnesn tonnesnwhil
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## nonprofit 3 5 17 1 0 0
## countri 9 7 5 11 12 12
## tonnesrelevancetypequantitycountsentimenttypeneutraldisambiguatedsubtypeentitiestext
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## tonnesrelevancetypequantityurlhttpwwwreuterscomarticlecolumnrussellcoalasiaiduslnxsfeedtyperssfeednamerbssenergynewstext
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## tonsrelevancetypequantitycountsentimenttypeneutraldisambiguatedsubtypeentitiestext
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## countri 11
## top total
## suggest 4 12
## alberta 0 8
## nonprofit 5 13
## countri 7 1
## totaltransactionsdocsentimentmixedscoretypenegativelanguageenglishentitiescountsentimentmixedscoretypepositivedisambiguatednamechinafreebasehttprdffreebasecomnsmdwsubtypelocationregionadministrativedivisiongovernmentaljurisdictionkingdomfilmscreeningvenuedbpediahttpdbpediaorgresourcechinaciafactbookhttpwwwwiwissfuberlindefactbookresourcechinageo
## suggest 1
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## totaltransactionsdocsentimentmixedscoretypenegativelanguageenglishentitiescountsentimentscoretypenegativedisambiguatedwebsitehttpwwwusagovyagohttpyagoknowledgeorgresourceunitedstatesnameunit
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## suggest 1
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## totaltransactionsdocsentimentmixedscoretypepositivelanguageenglishentitiescountsentimentmixedscoretypepositivedisambiguatedwebsitehttpenshenhuachinacomyagohttpyagoknowledgeorgresourcechinashenhuaenergycompanynamechina
## suggest 0
## alberta 4
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## totaltransactionsdocsentimentmixedscoretypepositivelanguageenglishentitiescountsentimentscoretypepositivedisambiguatedsubtypedbpediahttpdbpediaorgresourcerobincampbellpoliticiannamerobin
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## totaltransactionsdocsentimentmixedscoretypepositivelanguageenglishentitiescountsentimentscoretypepositivedisambiguatedwebsitehttpwwwaesocafreebasehttprdffreebasecomnsmczdbpediahttpdbpediaorgresourcealbertaelectricsystemoperatornamealberta
## suggest 1
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## totaltransactionsdocsentimentscoretypenegativelanguageenglishentitiesrelevancecounttypecountrysentimenttypeneutralentitiestextnorwayrelevancecounttypefieldterminologysentimenttypenegativescoreentitiestextgreenhous
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## tougher toward track traction trade
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## tradedbpediahttpdbpediaorgresourceinternationaltradefreebasehttprdffreebasecomnsmrvrelevanceconceptstextworld
## suggest 1
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## traffic trafficrelevancesentimentscoretypepositivekeywordstextco
## suggest 2 0
## alberta 2 4
## nonprofit 3 1
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## trafficrelevancesentimentscoretypepositivekeywordstextinexpens
## suggest 0
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## transcanada<U+0092> transform transit transport tree
## suggest 0 0 2 16 1
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## nonprofit 1 1 3 17 0
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## treesrelevancesentimenttypeneutralkeywordstextclim trend
## suggest 1 0
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## trillion tropic tuesday turn twh two twothird unbalanc unburn
## suggest 0 1 1 8 4 6 0 0 0
## alberta 4 5 3 4 0 2 4 4 4
## nonprofit 1 0 2 9 5 7 1 1 1
## countri 11 12 10 3 7 5 11 11 11
## uncertainti unchang underdevelop understand unit unite unlik
## suggest 2 0 0 0 1 16 1
## alberta 2 4 4 4 5 12 5
## nonprofit 3 1 1 1 0 17 0
## countri 9 11 11 11 12 5 12
## updat upon uptick urg usagovjejnjnth use
## suggest 1 0 1 1 1 31
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## nonprofit 2 1 0 0 2 32
## countri 10 11 12 12 10 20
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## valuesrelevancesentimenttypeneutralkeywordstextmayor vast versus
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## nonprofit 1 0 2
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## viabl vice vicepresid view vital voic volum wabamun waiver
## suggest 0 0 1 1 0 0 2 0 1
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## walter want warren wasn<U+0092>t way weak weaker wealth weather
## suggest 1 1 0 0 3 2 1 0 0
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## nonprofit 2 2 1 1 4 3 0 1 1
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## weatherrel
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## websitehttpindiagovinyagohttpyagoknowledgeorgresourceindiaopencychttpswopencycorgconceptmxrvvjxjwpebgdrcnyycaentitiestextindiarelevancetypecountrycountsentimenttypeneutraldisambiguatednamesouth
## suggest 1
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## weeksrelevancetypequantitycountsentimenttypeneutraldisambiguatedsubtypeentitiestext
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## alberta 4
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## countri 11
## weight well well<U+0094> well<U+0094>nth wellestablish west westmoreland
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## alberta 5 4 4 16 2 3 5 4 4
## nonprofit 0 1 1 21 3 2 0 1 1
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## without work worker
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## workersrelevancesentimenttypeneutralkeywordstextclim workforc
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## world worsen worth
## suggest 3 0 1
## alberta 1 4 5
## nonprofit 4 1 0
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## yagohttpyagoknowledgeorgresourceaustraliageonameshttpswsgeonamesorgopencychttpswopencycorgconceptmxrvviuswpebgdrcnyycaentitiestextaustraliarelevancetypecountrycountsentimentscoretypenegativedisambiguatednameasiafreebasehttprdffreebasecomnsmjksubtypelocationfieldofstudydbpediahttpdbpediaorgresourceasiayagohttpyagoknowledgeorgresourceasiaentitiestextasiarelevancetypecontinentcountsentimentscoretypenegativedisambiguatedsubtypeentitiestextcrud
## suggest 1
## alberta 5
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## yagohttpyagoknowledgeorgresourcelauncestontasmaniageonameshttpswsgeonamesorgentitiestextlauncestonrelevancetypecitycountsentimenttypeneutraldisambiguatedsubtypeentitiestextclyd
## suggest 1
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## countri 12
## year yearnon yearnother yearnpeabodi yearnsouth yearnwith
## suggest 44 1 1 1 1 1
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## nonprofit 45 2 0 2 0 0
## countri 33 10 12 10 12 12
## yearsnhowev yellowhead yetnwith york
## suggest 1 4 0 4
## alberta 5 0 4 0
## nonprofit 0 5 1 5
## countri 12 7 11 7
## yorkdisambiguatedwebsitehttpwwwnycgovyagohttpyagoknowledgeorgresourcenewyorkcitynamenew
## suggest 1
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## countri 10
## yuzhuo
## suggest 0
## alberta 4
## nonprofit 1
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## yuzhuorelevancetypepersoncountsentimenttypeneutraldisambiguatednamechinafreebasehttprdffreebasecomnsmdwsubtypelocationregionadministrativedivisiongovernmentaljurisdictionkingdomfilmscreeningvenuedbpediahttpdbpediaorgresourcechinaciafactbookhttpwwwwiwissfuberlindefactbookresourcechinageo
## suggest 0
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## zhang zhangrelevancesentimenttypeneutralkeywordstextchina
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## zieminski
## suggest 1
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## zieminskidisambiguatedsubtyperelevancecounttypecountrysentimenttypeneutralentitiestextindiadisambiguatedsubtypeurlhttpwwwreuterscomarticlepeabodyenergybankruptcyiduslnocvfeedtyperssfeednamerbssenergynewstext
## suggest 1
## alberta 3
## nonprofit 2
## countri 10
## Objective function: 38.43512
## Clustering vector: Named int [1:1499] 1 1 1 1 1 1 1 1 1 1 1 2 1 3 1 2 4 4 ...
## - attr(*, "names")= chr [1:1499] "<U+0092>re""| __truncated__ "<U+0092>ve""| __truncated__ "<U+0093>act""| __truncated__ "<U+0093>it<U+0092>s""| __truncated__ "<U+0093>make""| __truncated__ "<U+0093>some""| __truncated__ "<U+0093>you""| __truncated__ ...
## Cluster sizes: 899 149 336 115
## Best sample:
## [1] act
## [2] alberta
## [3] changeusagebi
## [4] countri
## [5] datanfellow
## [6] endors
## [7] nonprofit
## [8] northernsanta
## [9] slumpsourceworldconceptsrelevanceconceptstextpetroleumdbpediahttpdbpediaorgresourcepetroleumfreebasehttprdffreebasecomnsmrjrelevanceconceptstextcoaldbpediahttpdbpediaorgresourcecoalfreebasehttprdffreebasecomnsmsyopencychttpswopencycorgconceptmxrwprthpwpebgdrcnyycarelevanceconceptstextnatur
## [10] suggest
##
## Available components:
## [1] "sample" "medoids" "i.med" "clustering" "objective"
## [6] "clusinfo" "diss" "call" "silinfo" "data"